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Relationship: 1902

Title

The title of the KER should clearly define the two KEs being considered and the sequential relationship between them (i.e., which is upstream and which is downstream). Consequently all KER titles take the form “upstream KE leads to downstream KE”.  More help

Increased pro-inflammatory mediators leads to N/A, Breast Cancer

Upstream event
Upstream event in the Key Event Relationship. On the KER page, clicking on the Event name under Upstream Relationship will bring the user to that individual KE page. More help
Downstream event
Downstream event in the Key Event Relationship. On the KER page, clicking on the Event name under Upstream Relationship will bring the user to that individual KE page. More help

Key Event Relationship Overview

The utility of AOPs for regulatory application is defined, to a large extent, by the confidence and precision with which they facilitate extrapolation of data measured at low levels of biological organisation to predicted outcomes at higher levels of organisation and the extent to which they can link biological effect measurements to their specific causes. Within the AOP framework, the predictive relationships that facilitate extrapolation are represented by the KERs. Consequently, the overall WoE for an AOP is a reflection in part, of the level of confidence in the underlying series of KERs it encompasses. Therefore, describing the KERs in an AOP involves assembling and organising the types of information and evidence that defines the scientific basis for inferring the probable change in, or state of, a downstream KE from the known or measured state of an upstream KE. More help

AOPs Referencing Relationship

This table is automatically generated upon addition of a KER to an AOP. All of the AOPs that are linked to this KER will automatically be listed in this subsection. Clicking on the name of the AOP in the table will bring you to the individual page for that AOP. More help
AOP Name Adjacency Weight of Evidence Quantitative Understanding Point of Contact Author Status OECD Status
Increased reactive oxygen and nitrogen species (RONS) leading to increased risk of breast cancer adjacent Moderate Not Specified Jessica Helm (send email) Under development: Not open for comment. Do not cite Under Development
Increased DNA damage leading to increased risk of breast cancer adjacent Moderate Not Specified Jessica Helm (send email) Under development: Not open for comment. Do not cite Under Development

Taxonomic Applicability

Select one or more structured terms that help to define the biological applicability domain of the KER. In general, this will be dictated by the more restrictive of the two KEs being linked together by the KER. Authors can indicate the relevant taxa for this KER in this subsection. The process is similar to what is described for KEs (see pages 30-31 and 37-38 of User Handbook) More help

Sex Applicability

Authors can indicate the relevant sex for this KER in this subsection. The process is similar to what is described for KEs (see pages 31-32 of the User Handbook). More help

Life Stage Applicability

Authors can indicate the relevant life stage for this KER in this subsection. The process is similar to what is described for KEs (see pages 31-32 of User Handbook). More help

Key Event Relationship Description

Provide a brief, descriptive summation of the KER. While the title itself is fairly descriptive, this section can provide details that aren’t inherent in the description of the KEs themselves (see page 39 of the User Handbook). This description section can be viewed as providing the increased specificity in the nature of upstream perturbation (KEupstream) that leads to a particular downstream perturbation (KEdownstream), while allowing the KE descriptions to remain generalised so they can be linked to different AOPs. The description is also intended to provide a concise overview for readers who may want a brief summation, without needing to read through the detailed support for the relationship (covered below). Careful attention should be taken to avoid reference to other KEs that are not part of this KER, other KERs or other AOPs. This will ensure that the KER is modular and can be used by other AOPs. More help

Pro-inflammatory mediators increase the risk of breast cancer.

Evidence Supporting this KER

Assembly and description of the scientific evidence supporting KERs in an AOP is an important step in the AOP development process that sets the stage for overall assessment of the AOP (see pages 49-56 of the User Handbook). To do this, biological plausibility, empirical support, and the current quantitative understanding of the KER are evaluated with regard to the predictive relationships/associations between defined pairs of KEs as a basis for considering WoE (page 55 of User Handbook). In addition, uncertainties and inconsistencies are considered. More help

Biological Plausibility is Moderate. Tissue environment is known to be a major factor in carcinogenesis, and inflammatory processes are implicated in the development and invasiveness of breast and other cancers.

Empirical Evidence is Moderate. Interventions to increase inflammatory factors increase the carcinogenic potential of targeted and non-targeted cells. Inflammation is documented at earlier time points than tumorigenesis or invasion- within minutes or hours compared to days to months for carcinogenesis, consistent with an inflammatory mechanism of tumorigenesis and invasion. Inhibition of cytokines, inflammatory signaling pathways, and downstream effectors of inflammation activity prevent transformation, tumorigenesis, and invasion (including EMT and senescence) following IR or stimulation of inflammatory pathways. However, the key event and the adverse outcome differ in their dose-response to ionizing radiation: inflammation does not increase linearly with dose, while breast cancer and invasion does. Uncertainty arises from differences between the CBA/Ca mouse susceptible to leukemia from IR and the BALB/c mouse susceptible to mammary tumors from IR- the former has a pro-inflammatory response while the latter is apparently a mix of anti- and pro-inflammatory. This is a reminder that both pro- and anti-inflammatory factors may contribute to carcinogenesis- further research will be required to identify the context for each.

Biological Plausibility
Define, in free text, the biological rationale for a connection between KEupstream and KEdownstream. What are the structural or functional relationships between the KEs? For example, there is a functional relationship between an enzyme’s activity and the product of a reaction it catalyses. Supporting references should be included. However, it is recognised that there may be cases where the biological relationship between two KEs is very well established, to the extent that it is widely accepted and consistently supported by so much literature that it is unnecessary and impractical to cite the relevant primary literature. Citation of review articles or other secondary sources, like text books, may be reasonable in such cases. The primary intent is to provide scientifically credible support for the structural and/or functional relationship between the pair of KEs if one is known. The description of biological plausibility can also incorporate additional mechanistic details that help inform the relationship between KEs, this is useful when it is not practical/pragmatic to represent these details as separate KEs due to the difficulty or relative infrequency with which it is likely to be measured (see page 40 of the User Handbook for further information).   More help

Biological Plausibility is Moderate. Tissue environment is known to be a major factor in carcinogenesis, and inflammatory processes are implicated in the development and invasiveness of breast and other cancers. Studies suggest carcinogenic effects of IR extend beyond DNA damage and mutation of directly affected cells (Bouchard, Bouvette et al. 2013; Sridharan, Asaithamby et al. 2015; Barcellos-Hoff and Mao 2016), including indirect effects through exposed stroma of mammary gland (Nguyen, Oketch-Rabah et al. 2011; Nguyen, Fredlund et al. 2013; Illa-Bochaca, Ouyang et al. 2014). Inflammatory reactions offer one possible mechanism. Tumors and tumor cells exhibit features of inflammation, and inflammation is generally understood to promote transformation and tumor progression by supporting multiple hallmarks of cancer including oxidative activity and DNA damage, survival and proliferation, angiogenesis, and invasion and metastasis (Iliopoulos, Hirsch et al. 2009; Hanahan and Weinberg 2011; Esquivel-Velazquez, Ostoa-Saloma et al. 2015).

In photocarcinogenesis, cytokines and inflammatory signaling are implicated in immunosuppression and in promoting DNA damage via RONS (Valejo Coelho, Matos et al. 2016). In addition, inflammation related NF-kB, STAT3, COX2 and prostaglandins are implicated in the development and proliferation of skin cancers (Martens, Seebode et al. 2018).

Multiple cytokines and inflammatory pathways are implicated in mammary tumors and breast cancer.  Cytokines TGF-b and IL6 transform primary human mammospheres and pre-malignant mammary epithelial cell lines in vitro and make them tumorigenic in vivo (Sansone, Storci et al. 2007; Iliopoulos, Hirsch et al. 2009; Nguyen, Oketch-Rabah et al. 2011). IL6 is expressed by breast cancer fibroblasts and by fibroblasts from common sites of breast metastasis (breast, lung, and bone). IL6 is required for the growth and tumor promoting effects of fibroblasts from these sites on ER-positive (MCF-7) cancer cells in vitro and in vivo. IL6 can also promote the expression of IL6 in senescent (skin) fibroblasts and pre-malignant ER- breast epithelial cells (MCF10A). (Sasser, Sullivan et al. 2007; Studebaker, Storci et al. 2008). The growth and invasion-promoting effects of IL6 on primary non-cancer and cancer cell line (MCF-7) mammospheres in vitro depends on the activity of transcription factor NOTCH3, which supports the renewal of stem-like cell populations (Sansone, Storci et al. 2007). The inflammation-related transcription factor NF-kB contributes to mammary tumorigenesis and metastasis in PyVt mice (Connelly, Barham et al. 2011), and NF-kB/IL6/STAT3 activation is essential to mammosphere formation and migration in vitro as well as tumorigenesis from Src-activated or IL6 transformed MCF10 cells (Iliopoulos, Hirsch et al. 2009). The NF-kB/IL6/STAT3 signaling pathway generates cancer stem cells in multiple types of breast cancer cells lines and primary cancer cells (Iliopoulos, Hirsch et al. 2009; Iliopoulos, Jaeger et al. 2010; Iliopoulos, Hirsch et al. 2011) and is also implicated in colon and other cancers (Iliopoulos, Jaeger et al. 2010).

Uncertainties and Inconsistencies
In addition to outlining the evidence supporting a particular linkage, it is also important to identify inconsistencies or uncertainties in the relationship. Additionally, while there are expected patterns of concordance that support a causal linkage between the KEs in the pair, it is also helpful to identify experimental details that may explain apparent deviations from the expected patterns of concordance. Identification of uncertainties and inconsistencies contribute to evaluation of the overall WoE supporting the AOPs that contain a given KER and to the identification of research gaps that warrant investigation (seep pages 41-42 of the User Handbook).Given that AOPs are intended to support regulatory applications, AOP developers should focus on those inconsistencies or gaps that would have a direct bearing or impact on the confidence in the KER and its use as a basis for inference or extrapolation in a regulatory setting. Uncertainties that may be of academic interest but would have little impact on regulatory application don’t need to be described. In general, this section details evidence that may raise questions regarding the overall validity and predictive utility of the KER (including consideration of both biological plausibility and empirical support). It also contributes along with several other elements to the overall evaluation of the WoE for the KER (see Section 4 of the User Handbook).  More help

Uncertainty arises from the multifunctional nature of TGF-β, which may be anti- or pro-carcinogenic based on context, and around the contribution of inflammatory macrophages, which can differ based on genetic background. Further research is needed to isolate and identify the critical factors in these responses and their application in mammary gland.

TGF-β can be protective in a developmental context but may increase risk in another context. Increased baseline TGF-β decreases tumor incidence following lower doses of IR (0.1 Gy) in the SPRET outbred mouse, possibly by reducing ductal branching during development and subsequent susceptibility (Zhang, Lo et al. 2015). Conversely, the BALB/c mouse has lower baseline TGF-β during development but is susceptible to mammary tumors after IR, possibly via an elevated TGF-β response to IR. Early (4 hours) after low dose (0.075 Gy) IR these mice have suppressed immune pathways and macrophage response but increased IL6, COX2, and TGF-β pathway activation in mammary gland compared to the tumor-resistant C57BL/6 mouse (Snijders, Marchetti et al. 2012; Bouchard, Bouvette et al. 2013). By 1 week after IR BALB/c mammary glands show TGF-β-dependent inflammation, and by 1 month after IR they show proliferation (Nguyen, Martinez-Ruiz et al. 2011; Snijders, Marchetti et al. 2012). Consistent with this pattern, BALB/c mice that are heterozygous for TGF-β are more resistant to mammary tumorigenesis following IR (Nguyen, Oketch-Rabah et al. 2011). This pattern suggests that TGF-β is associated with inflammation, proliferation, and mammary tumorigenesis in these mice. However, the BALB/c mouse also has a polymorphism in a DNA repair gene associated with IR-induced genomic instability (Yu, Okayasu et al. 2001), making it difficult to distinguish potentially overlapping mechanisms.

Genetically susceptible mouse models offer somewhat conflicting information about the contribution of inflammation to cancer. In the CBA/Ca mouse susceptible to leukemia the macrophage response to IR is pro-inflammatory (M1 type) in contrast to the mammary tumor resistant C57BL/6 mouse, which develops anti-inflammatory M2type pro-phagocytic oxidative macrophages that target apoptotic cells (Lorimore, Coates et al. 2001; Lorimore, Chrystal et al. 2008). In contrast, in the BALB/c mouse susceptible to mammary tumors many inflammatory pathways and macrophages are suppressed early after IR, although there is also evidence of inflammation especially at later points (Nguyen, Martinez-Ruiz et al. 2011; Snijders, Marchetti et al. 2012; Bouchard, Bouvette et al. 2013).  It is possible that the two carcinogenic models represent two different mechanisms of susceptibility.

Finally, inflammation and other stromal factors alone are not sufficient to produce breast cancer. Studies in mice that support the importance of stromal context to IR tumorigenesis used epithelial cells with mutations in a DNA damage response gene p53. These transplant studies irradiate a mammary gland fat pad with epithelial cells removed, and transplant non-irradiated pre-malignant mutant (typically p53 mutant) epithelial cells (Barcellos-Hoff and Ravani 2000; Nguyen, Oketch-Rabah et al. 2011). Similar experiments showing NMU-treated stromal promotion of tumorigenesis use untreated primary epithelial cells sub-cultured repeatedly in vitro where some initiation could have taken place (Maffini, Soto et al. 2004), while in a similar experiment DMBA-treated stroma does not cause tumors from transplanted pre-malignant immortal cells (Medina and Kittrell 2005). This dependence on both stromal context and mutations to DNA damage response is consistent with contemporary ideas about the multi-factorial nature of carcinogenesis.

Response-response Relationship
This subsection should be used to define sources of data that define the response-response relationships between the KEs. In particular, information regarding the general form of the relationship (e.g., linear, exponential, sigmoidal, threshold, etc.) should be captured if possible. If there are specific mathematical functions or computational models relevant to the KER in question that have been defined, those should also be cited and/or described where possible, along with information concerning the approximate range of certainty with which the state of the KEdownstream can be predicted based on the measured state of the KEupstream (i.e., can it be predicted within a factor of two, or within three orders of magnitude?). For example, a regression equation may reasonably describe the response-response relationship between the two KERs, but that relationship may have only been validated/tested in a single species under steady state exposure conditions. Those types of details would be useful to capture.  More help
Time-scale
This sub-section should be used to provide information regarding the approximate time-scale of the changes in KEdownstream relative to changes in KEupstream (i.e., do effects on KEdownstream lag those on KEupstream by seconds, minutes, hours, or days?). This can be useful information both in terms of modelling the KER, as well as for analyzing the critical or dominant paths through an AOP network (e.g., identification of an AO that could kill an organism in a matter of hours will generally be of higher priority than other potential AOs that take weeks or months to develop). Identification of time-scale can also aid the assessment of temporal concordance. For example, for a KER that operates on a time-scale of days, measurement of both KEs after just hours of exposure in a short-term experiment could lead to incorrect conclusions regarding dose-response or temporal concordance if the time-scale of the upstream to downstream transition was not considered. More help
Known modulating factors
This sub-section presents information regarding modulating factors/variables known to alter the shape of the response-response function that describes the quantitative relationship between the two KEs (for example, an iodine deficient diet causes a significant increase in the slope of the relationship; a particular genotype doubles the sensitivity of KEdownstream to changes in KEupstream). Information on these known modulating factors should be listed in this subsection, along with relevant information regarding the manner in which the modulating factor can be expected to alter the relationship (if known). Note, this section should focus on those modulating factors for which solid evidence supported by relevant data and literature is available. It should NOT list all possible/plausible modulating factors. In this regard, it is useful to bear in mind that many risk assessments conducted through conventional apical guideline testing-based approaches generally consider few if any modulating factors. More help
Known Feedforward/Feedback loops influencing this KER
This subsection should define whether there are known positive or negative feedback mechanisms involved and what is understood about their time-course and homeostatic limits? In some cases where feedback processes are measurable and causally linked to the outcome, they should be represented as KEs. However, in most cases these features are expected to predominantly influence the shape of the response-response, time-course, behaviours between selected KEs. For example, if a feedback loop acts as compensatory mechanism that aims to restore homeostasis following initial perturbation of a KE, the feedback loop will directly shape the response-response relationship between the KERs. Given interest in formally identifying these positive or negative feedback, it is recommended that a graphical annotation (page 44) indicating a positive or negative feedback loop is involved in a particular upstream to downstream KE transition (KER) be added to the graphical representation, and that details be provided in this subsection of the KER description (see pages 44-45 of the User Handbook).  More help

Domain of Applicability

As for the KEs, there is also a free-text section of the KER description that the developer can use to explain his/her rationale for the structured terms selected with regard to taxonomic, life stage, or sex applicability, or provide a more generalizable or nuanced description of the applicability domain than may be feasible using standardized terms. More help

References

List of the literature that was cited for this KER description using the appropriate format. Ideally, the list of references should conform, to the extent possible, with the OECD Style Guide (OECD, 2015). More help

Andarawewa, K. L., S. V. Costes, et al. (2011). "Lack of radiation dose or quality dependence of epithelial-to-mesenchymal transition (EMT) mediated by transforming growth factor beta." International journal of radiation oncology, biology, physics 79(5): 1523-1531.

Andarawewa, K. L., A. C. Erickson, et al. (2007). "Ionizing radiation predisposes nonmalignant human mammary epithelial cells to undergo transforming growth factor beta induced epithelial to mesenchymal transition." Cancer Res 67(18): 8662-8670.

Barcellos-Hoff, M. H. and J. H. Mao (2016). "HZE Radiation Non-Targeted Effects on the Microenvironment That Mediate Mammary Carcinogenesis." Front Oncol 6: 57.

Barcellos-Hoff, M. H. and S. A. Ravani (2000). "Irradiated mammary gland stroma promotes the expression of tumorigenic potential by unirradiated epithelial cells." Cancer Res 60(5): 1254-1260.

Bisht, K. S., C. M. Bradbury, et al. (2003). "Inhibition of cyclooxygenase-2 with NS-398 and the prevention of radiation-induced transformation, micronuclei formation and clonogenic cell death in C3H 10T1/2 cells." Int J Radiat Biol 79(11): 879-888.

Bouchard, G., G. Bouvette, et al. (2013). "Pre-irradiation of mouse mammary gland stimulates cancer cell migration and development of lung metastases." British journal of cancer 109(7): 1829-1838.

Connelly, L., W. Barham, et al. (2011). "Inhibition of NF-kappa B activity in mammary epithelium increases tumor latency and decreases tumor burden." Oncogene 30(12): 1402-1412.

Ebrahimian, T. G., L. Beugnies, et al. (2018). "Chronic Exposure to External Low-Dose Gamma Radiation Induces an Increase in Anti-inflammatory and Anti-oxidative Parameters Resulting in Atherosclerotic Plaque Size Reduction in ApoE(-/-) Mice." Radiation research 189(2): 187-196.

Ehrhart, E. J., P. Segarini, et al. (1997). "Latent transforming growth factor beta1 activation in situ: quantitative and functional evidence after low-dose gamma-irradiation." FASEB journal : official publication of the Federation of American Societies for Experimental Biology 11(12): 991-1002.

El-Saghire, H., H. Thierens, et al. (2013). "Gene set enrichment analysis highlights different gene expression profiles in whole blood samples X-irradiated with low and high doses." Int J Radiat Biol 89(8): 628-638.

Elahi, E., N. Suraweera, et al. (2009). "Five quantitative trait loci control radiation-induced adenoma multiplicity in Mom1R Apc Min/+ mice." PLoS One 4(2): e4388.

Esquivel-Velazquez, M., P. Ostoa-Saloma, et al. (2015). "The role of cytokines in breast cancer development and progression." J Interferon Cytokine Res 35(1): 1-16.

Hanahan, D. and R. A. Weinberg (2011). "Hallmarks of cancer: the next generation." Cell 144(5): 646-674.

Iizuka, D., M. Sasatani, et al. (2017). "Hydrogen Peroxide Enhances TGFbeta-mediated Epithelial-to-Mesenchymal Transition in Human Mammary Epithelial MCF-10A Cells." Anticancer Res 37(3): 987-995.

Iliopoulos, D., H. A. Hirsch, et al. (2009). "An epigenetic switch involving NF-kappaB, Lin28, Let-7 MicroRNA, and IL6 links inflammation to cell transformation." Cell 139(4): 693-706.

Iliopoulos, D., H. A. Hirsch, et al. (2011). "Inducible formation of breast cancer stem cells and their dynamic equilibrium with non-stem cancer cells via IL6 secretion." Proceedings of the National Academy of Sciences of the United States of America 108(4): 1397-1402.

Iliopoulos, D., S. A. Jaeger, et al. (2010). "STAT3 activation of miR-21 and miR-181b-1 via PTEN and CYLD are part of the epigenetic switch linking inflammation to cancer." Mol Cell 39(4): 493-506.

Illa-Bochaca, I., H. Ouyang, et al. (2014). "Densely ionizing radiation acts via the microenvironment to promote aggressive Trp53-null mammary carcinomas." Cancer Res 74(23): 7137-7148.

Kim, E. S., M. S. Kim, et al. (2004). "TGF-beta-induced upregulation of MMP-2 and MMP-9 depends on p38 MAPK, but not ERK signaling in MCF10A human breast epithelial cells." Int J Oncol 25(5): 1375-1382.

Liakou, E., E. Mavrogonatou, et al. (2016). "Ionizing radiation-mediated premature senescence and paracrine interactions with cancer cells enhance the expression of syndecan 1 in human breast stromal fibroblasts: the role of TGF-beta." Aging (Albany NY) 8(8): 1650-1669.

Lorimore, S. A., J. A. Chrystal, et al. (2008). "Chromosomal instability in unirradiated hemaopoietic cells induced by macrophages exposed in vivo to ionizing radiation." Cancer Res 68(19): 8122-8126.

Lorimore, S. A., P. J. Coates, et al. (2001). "Inflammatory-type responses after exposure to ionizing radiation in vivo: a mechanism for radiation-induced bystander effects?" Oncogene 20(48): 7085-7095.

Maffini, M. V., A. M. Soto, et al. (2004). "The stroma as a crucial target in rat mammary gland carcinogenesis." J Cell Sci 117(Pt 8): 1495-1502.

Martens, M. C., C. Seebode, et al. (2018). "Photocarcinogenesis and Skin Cancer Prevention Strategies: An Update." Anticancer Res 38(2): 1153-1158.

Medina, D. and F. Kittrell (2005). "Stroma is not a major target in DMBA-mediated tumorigenesis of mouse mammary preneoplasia." J Cell Sci 118(Pt 1): 123-127.

Meeren, A. V., J. M. Bertho, et al. (1997). "Ionizing radiation enhances IL-6 and IL-8 production by human endothelial cells." Mediators Inflamm 6(3): 185-193.

Monceau, V., L. Meziani, et al. (2013). "Enhanced sensitivity to low dose irradiation of ApoE-/- mice mediated by early pro-inflammatory profile and delayed activation of the TGFbeta1 cascade involved in fibrogenesis." PLoS One 8(2): e57052.

Natarajan, M., C. F. Gibbons, et al. (2007). "Oxidative stress signalling: a potential mediator of tumour necrosis factor alpha-induced genomic instability in primary vascular endothelial cells." Br J Radiol 80 Spec No 1: S13-22.

Nguyen, D. H., E. Fredlund, et al. (2013). "Murine microenvironment metaprofiles associate with human cancer etiology and intrinsic subtypes." Clin Cancer Res 19(6): 1353-1362.

Nguyen, D. H., H. Martinez-Ruiz, et al. (2011). "Consequences of epithelial or stromal TGFbeta1 depletion in the mammary gland." J Mammary Gland Biol Neoplasia 16(2): 147-155.

Nguyen, D. H., H. A. Oketch-Rabah, et al. (2011). "Radiation acts on the microenvironment to affect breast carcinogenesis by distinct mechanisms that decrease cancer latency and affect tumor type." Cancer Cell 19(5): 640-651.

Park, C. C., R. L. Henshall-Powell, et al. (2003). "Ionizing radiation induces heritable disruption of epithelial cell interactions." Proc Natl Acad Sci U S A 100(19): 10728-10733.

Perrott, K. M., C. D. Wiley, et al. (2017). "Apigenin suppresses the senescence-associated secretory phenotype and paracrine effects on breast cancer cells." Geroscience 39(2): 161-173.

Sansone, P., G. Storci, et al. (2007). "IL-6 triggers malignant features in mammospheres from human ductal breast carcinoma and normal mammary gland." J Clin Invest 117(12): 3988-4002.

Sasser, A. K., N. J. Sullivan, et al. (2007). "Interleukin-6 is a potent growth factor for ER-alpha-positive human breast cancer." FASEB journal : official publication of the Federation of American Societies for Experimental Biology 21(13): 3763-3770.

Snijders, A. M., F. Marchetti, et al. (2012). "Genetic differences in transcript responses to low-dose ionizing radiation identify tissue functions associated with breast cancer susceptibility." PLoS One 7(10): e45394.

Sridharan, D. M., A. Asaithamby, et al. (2015). "Understanding cancer development processes after HZE-particle exposure: roles of ROS, DNA damage repair and inflammation." Radiat Res 183(1): 1-26.

Studebaker, A. W., G. Storci, et al. (2008). "Fibroblasts isolated from common sites of breast cancer metastasis enhance cancer cell growth rates and invasiveness in an interleukin-6-dependent manner." Cancer Res 68(21): 9087-9095.

Tsai, K. K., E. Y. Chuang, et al. (2005). "Cellular mechanisms for low-dose ionizing radiation-induced perturbation of the breast tissue microenvironment." Cancer Res 65(15): 6734-6744.

Valejo Coelho, M. M., T. R. Matos, et al. (2016). "The dark side of the light: mechanisms of photocarcinogenesis." Clin Dermatol 34(5): 563-570.

Yan, B., H. Wang, et al. (2006). "Tumor necrosis factor-alpha is a potent endogenous mutagen that promotes cellular transformation." Cancer Res 66(24): 11565-11570.

Yu, Y., R. Okayasu, et al. (2001). "Elevated breast cancer risk in irradiated BALB/c mice associates with unique functional polymorphism of the Prkdc (DNA-dependent protein kinase catalytic subunit) gene." Cancer Res 61(5): 1820-1824.

Zhang, P., A. Lo, et al. (2015). "Identification of genetic loci that control mammary tumor susceptibility through the host microenvironment." Sci Rep 5: 8919.