• About HMSR
    • Editors
    • Advisors
    • Partners
  • Latest
  • Archive
  • Submit
  • Contact
Menu

HMSR

Street Address
City, State, Zip
Phone Number
Harvard Medical Student Review

Your Custom Text Here

HMSR

  • About
    • About HMSR
    • Editors
    • Advisors
    • Partners
  • Latest
  • Archive
  • Submit
  • Contact

The Role of Estrogen in Ovarian Cancer and the Pathways by Which Estrogen Acts

December 4, 2023 HMS Review

“Two Women” by Edgar Degas. Courtesy National Gallery of Art, Washington.

Timothy Ehmann [1], Stephanie Barel [1], Amitabha Ray [2], Daniel Borsch [1]
[1] Lake Erie College of Osteopathic Medicine, Seton Hill University, Greensburg, Pennsylvania, 15601
[2] Alderson Broaddus University, Philippi, West Virginia, 26416, United States
Correspondence: TEhmann08651@med.lecom.edu   


ABSTRACT
Ovarian cancer remains a prevalent and deadly cancer in females. While anti-estrogen therapies have been useful in the treatment of other cancer types, their effectiveness in the treatment of ovarian cancers remains limited due to the limited understanding of estrogen’s effects on carcinogenesis and growth promotion in these tissues. This paper aims to summarize the role estrogen plays in ovarian cancer tumorigenesis and its potential value in targeted therapeutics. Estrogen’s effects are secondary to interactions with estrogen receptor (ER) α, ER β, and the G protein coupled receptor, GPR30. Genomic signaling of ER-αhas been shown to be primarily carcinogenic, while that of ER-βhas been shown to be a negative regulator of carcinogenesis. Additionally, ER-αhas been found to have carcinogenic effects through non-genomic signaling of the p53, MAPK, EGFR/Her2, PI3K, and IGF/IGFR pathways. GPR30’s effects have been found to be more variable and specific to tumor classification. For example, GPR30 is primarily carcinogenic in ovarian epithelial tumor types but appears to have protective effects when highly expressed in granulosa cell tumors. While the above generalizations can be made, a better understanding of estrogen’s effects on molecular signaling pathways will potentially allow for development of more effective targeted therapies against ovarian cancer.


INTRODUCTION
In the United States, ovarian cancer remains the 5th most common cancer among females today and the second most common gynecological/urinary (GU) cancer among females. Ovarian cancer had an estimated 21750 new cases in 2020 it ranks 4th in mortality among all cancers in females (1,2).

Stage I, low grade tumors may be managed with surgery and observation while stage II-IV lesions are typically managed with surgical debulking as well as multiple cycles of chemotherapy; 60% of ovarian cancer cases are stage III on detection (3,4).  Chemotherapy, which is typically in the form of platinum-based agents and taxane agents, and surgery still produce a 5 year survival rate of 85-90% for stage I, 57-70% for stage II, 39-59% for stage III, and 17% for stage IV (4). Unfortunately, anti-estrogen treatments are still in their early years with regards to the treatment of ovarian cancer and have shown limited efficacy (5), necessitating a better understanding of this pathway in cancer.

Estrogen interacts with three primary receptors: α, β, and a G protein coupled receptor, GPR30 (17, 26) (Table 1 defines the abbreviations/terms used throughout the paper). These receptors interact with downstream signaling pathways regulating, cell cycle progression, cell division, and migration (10, 37). These pathways may represent a novel target for ovarian cancer treatment, pending a better understanding of their role in carcinogenesis. The treatment of ovarian cancer requires further inroads into the understanding of the pathogenesis of these tumors and the effects of estrogen as it pertains to carcinogenesis and growth promotion. In theory, this will allow for the development of more effective targeted therapies.

Table 1: Definition of terms and abbreviations used throughout the text

Histology of ovarian carcinoma
There are numerous subtypes of ovarian carcinoma, each defined by the cells of origin. Table 2 summarizes some of the major characteristics/statistics regarding each subtype.

Table 2: In vitro and in vivo cell models used for testing of estrogen responsivity

ESTROGEN RECEPTORS AND CARCINOGENIC EFFECTS IN OVARIAN CANCER   Estrogen α receptor (ER-α)
The α receptor exists as one of three variants, based on variable splicing of mRNA: ER-α66, ER-α46, and ER-α36 (6, 7). ER-α66 is the primary receptor referenced in the literature. ER-α46 lacks the activating function domain, AF-1,while ER-α36 lacks both AF-1 and AF-2 domains; ER-α46 has been shown to be inhibitory towards ER-α66 in MCF-7 breast cancer cells (6,7). The α receptor (ER-α66) is commonly found in ovarian cancer, but shows a particular predominance in serous carcinomas. In a study performed by Sieh et.al (2013) (8), samples from 2933 females diagnosed with ovarian cancer were examined showing: 87.5% of low-grade serous carcinomas, 80.7% of high grade serous carcinomas, 76.6% of endometrioid carcinomas, 20.8% of mucinous carcinomas, and 19.4% of clear cell carcinomas stained positive for ER (1). In a smaller study of serous and mucinous carcinomas, 60% of these cancers had a ratio of estrogen receptor α:β greater than 1, meaning that cancer cells expressed a significantly greater number of estrogen receptor α molecules when compared to benign ovarian tissues (9). Furthermore, when exposed to estradiol, 5/16 ovarian cancer cell lines all PEO1 and PEO4 cells, staining strongly positive for ER α, showed increased growth compared to ER negative cell lines e.g., PEO14, PEO16, 41M, 59M, OVCAR-3, OVCAR-4, OVCAR-5, A2780, CAOV3 and OAW42 (10). ER positive staining ovarian serous carcinoma PEO4 cells have been shown to proliferate significantly more when exposed  to 17-B estradiol; ER negative PEO14 cells displayed no significant response to estradiol (11).

Estrogen receptor β (ER β)
The β receptor appears more frequently in normal ovarian tissue and benign tumors as opposed to malignant tissue/tumor (9, 12). The β receptor has multiple splicing variants: ERB1, ERB2, and ERB5. ERB1 is the primary isoform while ERB2 lacks ligand/DNA binding abilities and serves as a negative regulator of ER α; ERB5 serves a similar inhibitory function towards ER αand ERB1 (12, 13). ERB1 is thought to interfere with ER α activities, inhibit production of ER α, and inhibit cellular proliferation. These effects were exhibited in ER α positive, ER β negative BG-1 cells subsequently infected with adenovirus carrying the ER β gene (14).

Furthermore, ovarian cancer SKOV-3 cells expressing ERB1 exhibited decreased proliferation while caspase activity was found to be increased in ERB1 positive SKOV-3 cell lines compared to ERB1 negative SKOV-3 lines, increasing apoptosis (15). ER β receptor particularly the ERB1 isoform, seems to be protective against malignant evolution/progression. This is further elucidated by He et al, who showed that ER β agonist LY500307 decreased cell viability, promoted tumor suppressor gene expression, and increased apoptotic gene expression (89). Similarly, OSU-ERb-12, a ER β agonist, demonstrated both in vitro and in vivo inhibition of ovarian cell proliferation by inhibiting epithelial to mesenchymal transition, a key transition for malignancies (90). As such, the loss or suppression of ER β appears to be a key to malignant transformation.

GPR30
GPR30 is a membrane bound, G protein coupled receptor, sensitive to estradiol. The GPR30 receptor mediates signaling through multiple mechanisms (16, 17). The primary role of GPR30 in ovarian carcinogenesis is disputed currently. In BG-1 ovarian cancer cells, GPR30 was shown to activate the c-fos gene independently of ER α activity, suggesting that GPR30 may have effects on carcinogenesis independent of the estrogen receptor (17).

In ovarian cysts, higher levels of GPR30 mRNA were found in malignant lesions compared to benign, correlating with increased tumor size, advanced stage, and increased metastatic ability (18). Smith et. al (2009) showed that GPR30 was present more frequently in ovarian carcinoma than in lower risk ovarian tumors and correlated with a lower survival rate (19).

In granulosa cell tumors, GPR30 was found to be present in 53% of samples and correlated with poor survival in newly diagnosed cases (20). Comparison of 42 samples of various benign, borderline, and malignant epithelial ovarian carcinoma samples revealed that malignant samples were more likely to have increased amounts of GPR30 (21). However, GPR30 was shown to exhibit an anti-proliferative role in SKOV-3 and OVCAR-3 ovarian cancer cell lines. In these cells, GPR30 was found to mediate cell cycle arrest and inhibit cell proliferation (22).

MOLECULAR SIGNALING PATHWAYS
Estrogen receptors contain a ligand domain, a DNA binding domain, as well as domains for the binding and interaction with coactivators and corepressors (23). When estrogen enters the cell, it binds these receptors, inducing a conformational change and allowing for binding to estrogen response elements (ERE) within DNA as well as transcriptions factors (24, 25). Estrogen receptors can localize to the cytosol and nucleus, both initiated via a common pathway (26, 27). Cytosolic receptors, after binding estrogen, do not necessarily localize to the nucleus but rather associate with various other cytosolic proteins, affecting intracellular signaling cascades. ER activity can be divided into genomic and non-genomic, with interplay between the two groups (28).

Genomic activities of ER
Genomic signaling includes the activation of transcription factors and coactivators or corepressors via estrogen receptors α and β, as well as the direct binding of the ER to ERE regions within DNA. In ovarian cancer cells, genes directly regulated by estradiol through ERE regions include the genes for: AP4 DNA binding protein, cathepsin, cyclin B1, caspase 4, IGFBP3, cadherin 6, matrix metalloproteinases 11 and 17, in PEO1 cells, (10) and stromal cell-derived factor 1 (SDF-1) in BG-1 cells (29), as shown in Figure 1.

Figure 1: Through mRNA splicing, three variants of estrogen receptor α (ERα) exist: ERα66, ERα46, and ERα36. ERα66 interacts intranuclearly with DNA bound transcription factors AP-1 and SP-1, leading to both the upregulation of c-myc, IGF-1, and fibulin-1 as well as the downregulation of IL-6, tumor necrosis factor α (TNFα), follicle stimulating hormone β (FSHβ), and choline acetyltransferase.  Through a separate pathway, amplified in breast cancer-1 (AIB1) acts as a coactivator to ERα66, leading to the production of AP-4 (transcription factor), cathepsin, cyclin B1, caspase, cadherin 6, insulin-like growth factor binding protein 3 (IGFBP3), and matrix metalloproteinases 11 and 17 (MMP11/17).  Via the activating factor 2 (AF2) binding site, ERα46 acts intranuclearly to increase transcription and cell proliferation. Binding at activating factor 1 (AF1) inhibits ERα46 from this process.  Interactions with ERα66 inhibit ERα46 from entering the nucleus entirely. ERα36 lacks the AF1 and AF2 domains, and therefore is not involved in ovarian tumorigenesis. Three forms of estrogen receptor β (ERβ) are involved in ovarian tumorigenesis: ERβ1, ERβ2, and ERβ5.  ERβ1 inhibits ERα activity, leading to a decrease in ERα production as well as inhibition of cell proliferation. ERβ2 negatively regulates ERα within the cytoplasm, and as it has no ligand/DNA binding abilities, it does not directly act intranuclearly.  Similarly, ERβ5 acts as an ERα inhibitor, and has additional ERβ1 inhibitory effects.

Furthermore, a large family of p160 coactivators have been found to mediate ER/estrogen mediated genomic actions. This includes AIB1/SRC-3 and GRIP1 (24, 30).  AIB1 (SRC-3) has been shown to be overexpressed in BG-1 ovarian cancer cells. These same AIB1 overexpressing cells have shown increased transcription activity when treated with estradiol, eluding to AIB1’s role as a coactivator for ER (31). AIB1 is present in 68.7% of epithelial ovarian cancer samples with a significantly higher expression in cancerous tissue (32) and correlates with worse overall survival in epithelial cancer patients as well as higher grade tumors (33).

AP1 and Sp1 are two highly important transcription factors regulated by the estrogen receptors. Estrogen receptor α has been shown to bind and activate the AP1 complex, which requires intact activating function domains, AF-1 and AF-2. Interestingly, the estrogen β receptor lacks an AF-1 domain and as such only interacts with AP1 when treated with anti-estrogens, which normally block estrogen-α receptor mediated effects (30). Estradiol has been shown to have an inhibitory effect on the AP1 transcriptional pathway, suppressing expression of IL-6, TNF α, FSH β, and choline acetyltransferase (34) as shown in Figure 1. However, in CAOV-3, OVCAR-3, and A2780-ER ovarian cancer cells expressing ER α, estradiol was found to induce the expression of c-myc and IGF-I through an ER/estradiol/AP1 binding complex, suggesting a gene dependent ER mechanism (35), shown in Figure 1. Furthermore, components of AP-1, fos and jun proteins, have been found in elevated levels within ovarian tumors (36). Fibulin-1, a protein that plays a role in migration and motility of cells, has been shown to be overexpressed in BG-1, PEO4, and OVCAR-3 ovarian cancer cells and is responsive to estradiol treatment through ER α mediation (37). The fibulin-1 promoter was subsequently found to have 2 Sp1 binding sites, both required for estradiol/ER mediated fibulin transcription (37).

P53: Cross-talk between genomic and non-genomic
P53 serves as a “guardian” for the cellular genome, arresting the cell cycle, promoting DNA repair, and when necessary, promoting apoptosis. A large percentage of high grade serous and endometrioid carcinomas have shown loss of function mutations in p53, a gateway to carcinogenesis (38, 39). P53 has been shown to crosstalk with ER, forming a complex interaction between the two. ER α has been found to increase the transcription of the p53 gene within MCF7 breast cancer cells lines, both through a ligand dependent binding to an ERE in the p53 promoter (40) and through a calmodulin kinase IV mediated activation of NF-kB/CTF-1 transcription complex (41). P53 and its downstream mediator p21 have both been found to be upregulated with estradiol treatment within RhOSE ovarian cells as well (42). Furthermore, a polymorphism of p73, itself a homolog of p53 involved in the apoptosis of germ cells, has been found to be positively associated with increased ER positive human ovarian cancer development; this study surveyed epithelial, germ cell, and sex cord stromal cell tumors from human patients (43). However, ER α also plays an inhibitory role in regard to p53 regulated repression of survivin and MDR-1 (44) as well as p53 activation of ATF3, BTG2, and TRAF4 (45) as seen in Figure 2.

Figure 2: Mutations of the PTEN, RAS, and BRAF proteins can be found in multiple subtypes of ovarian carcinoma. PTEN down regulation/mutation leads to increased signaling in the PI3K pathway while mutations in RAS/BRAF lead to upregulation of the MAPK pathway. P53 exhibits a complex relationship with the estrogen receptor, increasing transcription of ERα, while ERα increases transcription of p53 with altered transcription of many p53 related genes.  

Wild type, non-mutated, p53 has been shown to increase the transcription of ER (46). In epithelial ovarian cancer, a mutated p53 protein that stabilizes wild type p53 was shown to increase levels of the ESR1 gene (47). It is hypothesized that the increased ESR1 expression is due to increased p53 mediated transcription of the ESR1 gene itself (47). However, owing to its anti-proliferative mechanism, a lack of p53 in mouse ovarian surface epithelial tumor cells correlated with estradiol mediated upregulation of growth and tumor invasion through upregulation of the estrogen receptor ESR1 (48). P53 and the estrogen receptor have a complex relationship, each increasing the transcription of the other while simultaneously inhibiting the transcriptional activity of that same molecule, preventing its downstream effects. A similar paradox can be seen in type 2 diabetes mellitus induced insulin resistance, in which high glucose levels drives increased insulin secretion but this subsequently results in resistance to insulin’s effects.

Non-genomic activities of ER
Non genomic activities include those in which the estrogen receptor interacts with proteins other than transcription factors and do not immediately affect the genome.  Ultimately many of these pathways lead to genomic alterations in the form of upregulation/downregulation of various genes, but begin independently of the genome. Table 3 details some of the therapies currently being studied, targeting these pathways.

Table 3: Summary of Treatments Targeting Non-Genomic Pathways

MAPK pathway and ER
Mutations in both RAS and BRAF are present in higher frequency in low grade serous and mucinous ovarian carcinomas, upregulating the MAPK pathway, illustrated in Figure 2 (49, 50). Rap1A, a RAS associated protein, has been found to increase cell proliferation, migration, and invasion in human HEYA8 ovarian cancer cells. This carcinogenic activity appeared to be mediated by induced expression of MAPK pathways constituents, MEK1 and 2, as well as ERK 1 and 2, illustrated in Figure 3 (51). Furthermore, high amounts of phosphorylated/active MAPK have been observed in high grade serous carcinomas, corresponding to a worse survival rate (52). This increased level of MAPK correlated with a higher level of EGFR as well (52). Inhibition of the MAPK pathway, through the use of Aloesin (53) and delphinidin (54), has actually been shown to decrease the growth of SKOV3 ovarian cancer cells. The inhibition of Src kinase, which acts as a key regulator of both the MAPK and EGFR mediated pathways, has been investigated as well. Src was found to be highly expressed in ovarian cancer cell lines activated by the estrogen/ER complex (55). Subsequently, inhibition of Src inhibited cancer growth and reduced levels of c-myc expression within PEO1R and BG-1 ovarian cancer cells (55) seen in Figure 3.

Figure 3: Estrogen receptor α (ERα) is involved in ovarian tumorigenesis and cellular proliferation through multiple non-genomic signaling pathways. Intranuclear effects of ERα lead to the upregulation of p53 as well as downregulation of survivin, multi-drug resistance gene 1 (MDR-1), activating transcription factor 3 (ATF3), B-cell translocation gene 2 (BTG2), and tumor necrosis factor [TNF] receptor-associated factor 4 (TRAF4). This process is enhanced via human epidermal growth factor receptor 2 (Her2) activity at the cell membrane. ERα directly increases activity of Akt (protein kinase B) and steroid hormone co-receptor (Src), and exerts similar action indirectly through the insulin receptor substrate/insulin-like growth factor receptor (IRS-1/IGFR) pathway. Downstream, Akt upregulates α-actinin 4 and hypoxia inducible factor 1 (HIF1), and downregulates the tumor suppressor gene, nm23-H1, and E-cadherin. Src upregulates both c-myc and extracellular signal related kinases 1 and 2 (ERK1/2). Furthermore, the G protein coupled receptor 30 (GPR30)/ERα co-dependent mechanism leads to cell proliferation via upregulation of c-fos and cyclins (A, D1, and E) as well as the epidermal growth factor receptor/extracellular related kinase (EGFR/ERK) pathway.  Lastly, intranuclear p53 acts on DNA upregulating the production of ERα.

EGFR/Her2 and ER
Her2/neu is a growth factor receptor commonly associated with carcinogenesis and has been shown to bind membrane associated ER, modulating its ligand responsive properties (56, 57). Her2 interacts with the MAPK pathway and has been shown to activate both nuclear ER and its coactivator AIB1 in order to increase ER mediated transcription (58). Therefore, the presence of Her2/neu in various carcinomas may enhance estrogen’s role in carcinogenesis and tumor progression. High levels of Her2 have been reported in ovarian cancer, which reportedly caused increased stimulation of both MAPK and PI3K pathways. However, when inhibited by pertuzumab (a monoclonal anti-Her2 dimerization antibody), Her2 and ER α mediated activity is inhibited (59). Furthermore, EGFR has been found to be more highly expressed in ovarian cancer tissue when compared to normal ovarian tissue, with a 56.8% difference between the two groups (60). Higher EGFR expression in malignant ovarian tissue corresponds to a worse survival rate, owing to the anti-apoptotic effects of EGFR (61). As shown in Figure 3, EGFR gain of function mutations were found to promote increased phosphorylation of both Akt and ERK in ovarian cancer (62), ERK playing a role in the estrogen responsive MAPK pathway already mentioned above.

PI3K and ER
Lower grade endometrioid as well as clear cell carcinomas of the ovary showed higher rates of PTEN and PI3K pathway mutations, leading to an upregulation of this pathway and carcinogenesis (49, 39). In cases of low grade carcinoma, CTNNB1 (16-38% of cases) and PTEN (14-21% of cases) mutations result in increased activity of the B-catenin protein, causing unregulated cell proliferation through the PI3K cascade illustrated in Figure 2. However, in cases of high grade endometrioid carcinoma, mutations in P53 were found to be more prevalent (60% of cases), ultimately leading to unregulated DNA replication and cell proliferation (39). Shown in Figure 3, downstream of PI3K, Akt/pAkt and mTOR/pmTOR were shown to be phosphorylated in 55% of ovarian cancer tissue regulating transcription of Bcl-2 and survivin (63, 64). In ES-2 and SKOV-3 ovarian cancer cells, estradiol was found to reduce the expression of the nm23-H1 tumor suppressor gene (65). This inhibition of nm23-H1 expression was found to be mediated by pAkt, pointing to a PI3K/Akt mediated pathway in ovarian carcinogenesis (65). Furthermore, α-actinin 4, a metastatic promoter, and E-cadherin, a tumor suppressor gene, are both regulated by the PI3K pathway (fig 3). In SKOV3 cells, positive for both the α and β receptor, treatment with estradiol caused an increase in pAkt and increased α-actinin 4 expression, with concurrent decreased E-cadherin expression and increase in growth and migration (66). Another tumorigenic promoter, HIF-1α, expressed under low oxygen conditions, was found to be elevated post-estradiol treatment in ES-2 and SKOV3 cells, promoting cell proliferation. This elevation was found to be dependent on estradiol’s activation of Akt (67).

IGF/IGFR and ER
IGF is involved in normal function and development of many tissues including the ovary. Given its role in steroidogenesis and overall cell growth, it has been thought that IGF-I and IGF-II, as well as their receptors and the related 6 IGF binding proteins (IGFBP), might play a role in carcinogenesis. IGF-I has been found to facilitate proliferation, invasion and angiogenesis in tumor cells and IGF-II has been shown to mediate cell adhesion and invasion in ovarian cancer (68). IGFBPs play an inhibitory role in normal physiologic development, acting as a trap for IGF-I and IGF-II (69), but in ovarian cancer, a downregulation of IGFBP3 and 5 with a concurrent increase in IGFBP4 (70) and IGFBP2 (71) levels has been observed. The mechanism behind this selective regulation of IGF binding proteins is unknown but has been suggested to be a result of ER α influence (72). Furthermore, as shown in Figure 3, the IGF-I receptor has been found to upregulate both the MAPK and PI3K pathways mentioned above (73-76), as well as downstream constituents Akt and ERK1 and ERK2 (68). This signaling generally occurs through the phosphorylation of the insulin receptor substrate-1 (IRS-1), which has been shown to be activated by the estradiol/ER α complex (73).

GPR30 Receptor
The G protein coupled receptor is referred to as either GPR30 or GPER1 and both labels will be used interchangeably in the subsequent section. The ligands to which GPR30 responds include estradiol, G-1 (G protein coupled receptor agonist), as well as tamoxifen and other estrogen agonists (77). GPR30 has been found to localize primarily in 2 locations: the plasma membrane and the endoplasmic reticulum (78, 77), with estrogen responsivity at both locations. Ultimately, GPR30 exerts its effects in ovarian cancer through calcium mobilization (77) and EGFR transactivation with subsequent MAPK activation (79,80). GPR30 expression is higher in ovarian carcinomas, as opposed to benign and borderline malignancies, with its highest expression in serous, endometrioid and mucinous tumors (80). In BG-1 ovarian cancer cells, EGFR/ERK activation was found to be dependent on both ER α and GPR30, effectively promoting cell proliferation (79). Furthermore, estradiol was found to upregulate cyclins A, E, and D1 as well as the c-fos gene, through this same ER α/GPR30 mechanism, further promoting proliferation and cell cycle progression (79), shown in Figure 3. GPR30/EGFR co-expression in ovarian carcinomas has been shown to correlate with worse survival rates in these tumors (80). Paradoxically, GPER1 has been found to be highly expressed in granulosa cell tumors and was found to be associated with reduced migration and invasion in these tumors specifically. This reduced migratory/invasive property was shown to be mediated by GPER1 and repression of ERK1 and ERK2 (81).

CONCLUSIONS
Estrogen has been linked to breast, endometrial, and ovarian cancer development. Its involvement in the pathogenesis of ovarian cancer is complicated, given the presence of ER α and β, as well as GPR30 and the individual mechanisms by which each acts. ER-αhas been shown to be primarily carcinogenic while ER-β is a known negative regulator of carcinogenesis; GPR30 is primarily carcinogenic but there is evidence to suggest it could be a negative regulator of the cell cycle as well in ovarian tissue. Furthermore, these receptors interact with the p53, MAPK, EGFR/Her2, PI3K, and IGF/IGFR pathways, with unique interactions with each pathway. Estrogen and its effects in ovarian cancer are still poorly characterized, particularly regarding these molecular signaling pathways, an area of research that may yield unique insights into treatment of this disease.

DISCLOSURES
Funding: Not applicable.
Conflicts of interest: None.
Availability of data and materials: Not applicable.
Code availability: Not applicable.
Ethics approval: Not applicable.
Consent to participate: Not applicable.

REFERENCES
1.     Siegel, R.L., Miller, K.D. and Jemal, A. (2020). Cancer statistics, 2020. CA: a cancer journal for clinicians, 70(1), 7–30.

2.     Ehmann T, Barel S, Xu G, et al. (2019). A comparative analysis of estrogenic pathways between ovarian and cervical cancers. Proceedings of the LECOM Inter-Professional Research Day (Erie, PA), A7.

3.     Orr, B. and Edwards, R.P. (2018). Diagnosis and treatment of ovarian cancer. Hematology/Oncology Clinics, 32(6), 943-964.

4.     Howlader N, Noone AM, Krapcho M, et al. (2020). SEER Cancer Statistics Review, 1975-2017 [Online]. Available from: https://seer.cancer.gov/archive/csr/1975_2017/. [Accessed Aug 1 2021]

5.     Simpkins, F., Garcia-Soto, A. and Slingerland, J. (2013). New insights on the role of hormonal therapy in ovarian cancer. Steroids, 78(6), 530-537.

6.     Flouriot, G., Brand, H., Denger, S., et al. (2000). Identification of a new isoform of the human estrogen receptor-alpha (hER-α) that is encoded by distinct transcripts and that is able to repress hER-α activation function 1. The EMBO journal, 19(17), 4688-4700.

7.     Shi, L., Dong, B., Li, Z., et al. (2009). Expression of ER-α36, a novel variant of estrogen receptor α, and resistance to tamoxifen treatment in breast cancer. Journal of clinical Oncology, 27(21), 3423.

8.     Sieh, W., Köbel, M., Longacre, T.A., et al. (2013). Hormone-receptor expression and ovarian cancer survival: an Ovarian Tumor Tissue Analysis consortium study. The lancet oncology, 14(9), 853-862.

9.     Pujol, P., Rey, J.M., Nirde, P., et al. (1998). Differential expression of estrogen receptor-α and-β messenger RNAs as a potential marker of ovarian carcinogenesis. Cancer research, 58(23), 5367-5373.

10.   O’Donnell, A.J., Macleod, K.G., Burns, D.J., et al. (2005). Estrogen receptor-α mediates gene expression changes and growth response in ovarian cancer cells exposed to estrogen. Endocrine-related cancer, 12(4), 851-866.

11.   Langdon, S.P., Hawkes, M.M., Lawrie, S.S., et al. (1990). Oestrogen receptor expression and the effects of oestrogen and tamoxifen on the growth of human ovarian carcinoma cell lines. British Journal of Cancer, 62(2), 213-216.

12.   Voutsadakis, I.A. (2016). Hormone receptors in serous ovarian carcinoma: prognosis, pathogenesis, and treatment considerations. Clinical Medicine Insights: Oncology, 10, CMO-S32813.

13.   Herynk, M.H. and Fuqua, S.A. (2004). Estrogen receptor mutations in human disease. Endocrine reviews, 25(6), 869-898.

14.   Bossard, C., Busson, M., Vindrieux, D., et al. (2012). Potential Role of Estrogen Receptor Beta as a Tumor Suppressor of Epithelial Ovarian Cancer. PLoS ONE, 7(9), e44787.

15.   Treeck, O., Pfeiler, G., Mitter, D., et al. (2007). Estrogen receptor β1 exerts antitumoral effects on SK-OV-3 ovarian cancer cells. Journal of Endocrinology, 193(3), 421-433.

16.   Prossnitz, E.R. and Barton, M. (2014). Estrogen biology: new insights into GPER function and clinical opportunities. Molecular and cellular endocrinology, 389(1-2), 71-83.

17.   Albanito, L., Madeo, A., Lappano, R., et al. (2007). G protein–coupled receptor 30 (GPR30) mediates gene expression changes and growth response to 17β-estradiol and selective GPR30 ligand G-1 in ovarian cancer cells. Cancer research, 67(4), 1859-1866.

18.   Long, L., Cao, Y. and Tang, L.D. (2012). Transmembrane estrogen receptor GPR30 is more frequently expressed in malignant than benign ovarian endometriotic cysts and correlates with MMP-9 expression. International Journal of Gynecologic Cancer, 22(4).

19.   Smith, H.O., Arias-Pulido, H., Kuo, D.Y., et al. (2009). GPR30 predicts poor survival for ovarian cancer. Gynecologic oncology, 114(3), 465-471.

20.   Heublein, S., Mayr, D., Friese, K., et al. (2014). The G-protein-coupled estrogen receptor (GPER/GPR30) in ovarian granulosa cell tumors. International journal of molecular sciences, 15(9), 15161-15172.

21.   Kolkova, Z., Casslén, V., Henic, E., et al. (2012). The G protein-coupled estrogen receptor 1 (GPER/GPR30) does not predict survival in patients with ovarian cancer. Journal of ovarian research, 5(1), 1-11.

22.   Ignatov, T., Modl, S., Thulig, M., et al. (2013). GPER-1 acts as a tumor suppressor in ovarian cancer. Journal of ovarian research, 6(1), 1-10.

23.   Katzenellenbogen, B.S., (1996). Estrogen receptors: bioactivities and interactions with cell signaling pathways. Biology of reproduction, 54(2), 287-293.

24.   O’Malley, B.W. (2005). A life-long search for the molecular pathways of steroid hormone action. Molecular Endocrinology, 19(6), 1402-1411.

25.   Weigel, N.L. (1996). Steroid hormone receptors and their regulation by phosphorylation. Biochemical Journal, 319(3), 657-667.

26.   Pietras, R.J. and Márquez-Garbán, D.C. (2007). Membrane-associated estrogen receptor signaling pathways in human cancers. Clinical Cancer Research, 13(16), 4672-4676.

27.   Razandi, M., Pedram, A., Greene, G.L. et al. (1999). Cell membrane and nuclear estrogen receptors (ERs) originate from a single transcript: studies of ERα and ERβ expressed in Chinese hamster ovary cells. Molecular endocrinology, 13(2), 307-319.

28.   Bjornstrom, L. and Sjoberg, M. (2005). Mechanisms of estrogen receptor signaling: convergence of genomic and nongenomic actions on target genes. Molecular endocrinology, 19(4), 833-842.

29.   Hall, J.M. and Korach, K.S. (2003). Stromal cell-derived factor 1, a novel target of estrogen receptor action, mediates the mitogenic effects of estradiol in ovarian and breast cancer cells. Molecular Endocrinology, 17(5), 792-803.

30.   Webb, P., Nguyen, P., Valentine, C., et al. (1999). The estrogen receptor enhances AP-1 activity by two distinct mechanisms with different requirements for receptor transactivation functions. Molecular Endocrinology, 13(10), 1672-1685.

31.   Anzick, S.L., Kononen, J., Walker, R.L., et al. (1997). AIB1, a steroid receptor coactivator amplified in breast and ovarian cancer. Science, 277(5328), 965-968.

32.   Liang, M. and Zhao, J. (2018). Protein expressions of AIB1, p53 and Bcl-2 in epithelial ovarian cancer and their correlations with the clinical pathological features and prognosis. Eur Rev Med Pharmacol Sci, 22(16), 5134-5139.

33.   Palmieri, C., Gojis, O., Rudraraju, B., et al. (2013). Expression of steroid receptor coactivator 3 in ovarian epithelial cancer is a poor prognostic factor and a marker for platinum resistance. British journal of cancer, 108(10), 2039-2044.

34.   Jakacka, M., Ito, M., Weiss, J, et al. (2001). Estrogen receptor binding to DNA is not required for its activity through the nonclassical AP1 pathway. Journal of Biological Chemistry, 276(17), 13615-13621.

35.   Sasaki, H., Hayakawa, J., Terai, Y., et al. (2008). Difference between genomic actions of estrogen versus raloxifene in human ovarian cancer cell lines. Oncogene, 27(19), 2737-2745.

36.   Hein, S., Mahner, S., Kanowski, C., et al. (2009). Expression of Jun and Fos proteins in ovarian tumors of different malignant potential and in ovarian cancer cell lines. Oncology reports, 22(1), 177-183.

37.   Bardin, A., Moll, F., Margueron, R., et al. (2005). Transcriptional and posttranscriptional regulation of fibulin-1 by estrogens leads to differential induction of messenger ribonucleic acid variants in ovarian and breast cancer cells. Endocrinology, 146(2), 760-768.

38.   Cotran, R., Kumar, V. and Robbins, S. (2015). Pathologic basis of disease. Philadelphia, PA: Saunders Elsevier.

39.   Cho, K.R. and Shih, I.M. (2009). Ovarian cancer. Annual review of pathology: mechanisms of disease, 4, 287-313.

40.   Berger, C.E., Qian, Y., Liu, G., et al. (2012). p53, a target of estrogen receptor (ER) α, modulates DNA damage-induced growth suppression in ER-positive breast cancer cells. Journal of Biological Chemistry, 287(36), 30117-30127.

41.   Qin, C., Nguyen, T., Stewart, J., et al. (2002). Estrogen up-regulation of p53 gene expression in MCF-7 breast cancer cells is mediated by calmodulin kinase IV-dependent activation of a nuclear factor κB/CCAAT-binding transcription factor-1 complex. Molecular Endocrinology, 16(8), 1793-1809.

42.   Wright, J.W., Stouffer, R.L. and Rodland, K.D. (2005). High-dose estrogen and clinical selective estrogen receptor modulators induce growth arrest, p21, and p53 in primate ovarian surface epithelial cells. The Journal of Clinical Endocrinology & Metabolism, 90(6), 3688-3695.

43.   Guan, X., Zhang, N., Yin, Y., et al. (2012). Polymorphisms in the p63 and p73 genes are associated with ovarian cancer risk and clinicopathological variables. Journal of Experimental & Clinical Cancer Research, 31(1), 1-8.

44.   Sayeed, A., Konduri, S.D., Liu, W., et al. (2007). Estrogen receptor α inhibits p53-mediated transcriptional repression: implications for the regulation of apoptosis. Cancer research, 67(16), 7746-7755.

45.   Bailey, S.T., Shin, H., Westerling, T., et al. (2012). Estrogen receptor prevents p53-dependent apoptosis in breast cancer. Proceedings of the National Academy of Sciences, 109(44), 18060-18065.

46.   Berger, C., Qian, Y. and Chen, X. (2013). The p53-estrogen receptor loop in cancer. Current molecular medicine, 13(8), 1229-1240.

47.   Ren, Y.A., Mullany, L.K., Liu, Z., et al. (2016). Mutant p53 promotes epithelial ovarian cancer by regulating tumor differentiation, metastasis, and responsiveness to steroid hormones. Cancer research, 76(8), 2206-2218.

48.   Mullany, L.K., Liu, Z., Wong, K.K., et al. (2014). Tumor repressor protein 53 and steroid hormones provide a new paradigm for ovarian cancer metastases. Molecular Endocrinology, 28(1), 127-137.

49.   Rosen, D.G., Yang, G., Liu, G., et al. (2009). Ovarian cancer: pathology, biology, and disease models. Frontiers in bioscience: a journal and virtual library, 14, 2089.

50.   Ho, C.L., Kurman, R.J., Dehari, R., et al. (2004). Mutations of BRAF and KRAS precede the development of ovarian serous borderline tumors. Cancer research, 64(19), 6915-6918.

51.   Lu, L., Wang, J., Wu, Y., et al. (2016). Rap1A promotes ovarian cancer metastasis via activation of ERK/p38 and notch signaling. Cancer medicine, 5(12), 3544-3554.

52.   Hew, K.E., Miller, P.C., El-Ashry, D., et al. (2016). MAPK activation predicts poor outcome and the MEK inhibitor, selumetinib, reverses antiestrogen resistance in ER-positive high-grade serous ovarian cancer. Clinical Cancer Research, 22(4), 935-947.

53.   Zhang, L.Q., Lv, R.W., Qu, X.D., et al. (2017). Aloesin suppresses cell growth and metastasis in ovarian cancer SKOV3 cells through the inhibition of the MAPK signaling pathway. Analytical Cellular Pathology, 2017, 8158254.

54.   Lim, W. and Song, G. (2017). Inhibitory effects of delphinidin on the proliferation of ovarian cancer cells via PI3K/AKT and ERK 1/2 MAPK signal transduction. Oncology Letters, 14(1), 810-818.

55.   Simpkins, F., Hevia-Paez, P., Sun, J., et al. (2012). Src Inhibition with saracatinib reverses fulvestrant resistance in ER-positive ovarian cancer models in vitro and in vivo. Clinical cancer research, 18(21), 5911-5923.

56.   Chung, Y.L., Sheu, M.L., Yang, S.C., et al. (2002). Resistance to tamoxifen‐induced apoptosis is associated with direct interaction between Her2/neu and cell membrane estrogen receptor in breast cancer. International journal of cancer, 97(3), 306-312.

57.   Arpino, G., Wiechmann, L., Osborne, C.K. et al. (2008). Crosstalk between the estrogen receptor and the HER tyrosine kinase receptor family: molecular mechanism and clinical implications for endocrine therapy resistance. Endocrine reviews, 29(2), 217-233.

58.   Osborne, C.K., Bardou, V., Hopp, T.A., et al. (2003). Role of the estrogen receptor coactivator AIB1 (SRC-3) and HER-2/neu in tamoxifen resistance in breast cancer. Journal of the National Cancer Institute, 95(5), 353-361.

59.   Mullen, P., Cameron, D.A., Hasmann, M., et al. (2007). Sensitivity to pertuzumab (2C4) in ovarian cancer models: cross-talk with estrogen receptor signaling. Molecular cancer therapeutics, 6(1), 93-100.

60.   Ranjbar, R., Nejatollahi, F., Ahmadi, A.S.N., et al. (2015). Expression of vascular endothelial growth factor (VEGF) and epidermal growth factor receptor (EGFR) in patients with serous ovarian carcinoma and their clinical significance. Iranian journal of cancer prevention, 8(4).

61.   Psyrri, A., Kassar, M., Yu, Z., et al. (2005). Effect of epidermal growth factor receptor expression level on survival in patients with epithelial ovarian cancer. Clinical Cancer Research, 11(24), 8637-8643.

62.   Tanaka, Y., Terai, Y., Tanabe, A., et al. (2011). Prognostic effect of epidermal growth factor receptor gene mutations and the aberrant phosphorylation of Akt and ERK in ovarian cancer. Cancer biology & therapy, 11(1), 50-57.

63.   Altomare, D.A., Wang, H.Q., Skele, K.L., et al. (2004). AKT and mTOR phosphorylation is frequently detected in ovarian cancer and can be targeted to disrupt ovarian tumor cell growth. Oncogene, 23(34), 5853-5857.

64.   Cheaib, B., Auguste, A. and Leary, A. (2015). The PI3K/Akt/mTOR pathway in ovarian cancer: therapeutic opportunities and challenges. Chinese journal of cancer, 34(1), 4-16.

65.   Hua, K., Feng, W., Cao, Q., et al. (2008). Estrogen and progestin regulate metastasis through the PI3K/AKT pathway in human ovarian cancer. International journal of oncology, 33(5), 959-967.

66.   Lu, Z., Zhang, Y., Yan, X., et al. (2014). Estrogen stimulates the invasion of ovarian cancer cells via activation of the PI3K/AKT pathway and regulation of its downstream targets E-cadherin and α-actinin-4. Molecular Medicine Reports, 10(5), 2433-2440.

67.   Hua, K., Din, J., Cao, Q., et al. (2009). Estrogen and progestin regulate HIF-1α expression in ovarian cancer cell lines via the activation of Akt signaling transduction pathway. Oncology reports, 21(4), 893-898.

68.   Beauchamp, M.C., Yasmeen, A., Knafo, A. et al. (2010). Targeting insulin and insulin-like growth factor pathways in epithelial ovarian cancer. Journal of oncology, 2010, 257058.

69.   Foulstone, E., Prince, S., Zaccheo, O., et al. (2005). Insulin‐like growth factor ligands, receptors, and binding proteins in cancer. The Journal of Pathology: A Journal of the Pathological Society of Great Britain and Ireland, 205(2), 145-153.

70.   Walker, G., MacLeod, K., Williams, A.R., et al. (2007). Insulin-like growth factor binding proteins IGFBP3, IGFBP4, and IGFBP5 predict endocrine responsiveness in patients with ovarian cancer. Clinical Cancer Research, 13(5), 1438-1444.

71.   Flyvbjerg, A., Mogensen, O., Mogensen, B., et al. (1997). Elevated serum insulin-like growth factor-binding protein 2 (IGFBP-2) and decreased IGFBP-3 in epithelial ovarian cancer: correlation with cancer antigen 125 and tumor-associated trypsin inhibitor. The Journal of Clinical Endocrinology & Metabolism, 82(7), 2308-2313.

72.   Lu, L., Katsaros, D., Wiley, A., et al. (2006). The relationship of insulin-like growth factor-II, insulin-like growth factor binding protein-3, and estrogen receptor-alpha expression to disease progression in epithelial ovarian cancer. Clinical Cancer Research, 12(4), 1208-1214.

73.   Kahlert, S., Nuedling, S., Van Eickels, M., et al. (2000). Estrogen receptor α rapidly activates the IGF-1 receptor pathway. Journal of Biological Chemistry, 275(24), 18447-18453.

74.   Chen, J., Zhao, K.N., Li, R., et al. (2014). Activation of PI3K/Akt/mTOR pathway and dual inhibitors of PI3K and mTOR in endometrial cancer. Current medicinal chemistry, 21(26), 3070-3080.

75.   Fukushima, T., Nakamura, Y., Yamanaka, D., et al. (2012). Phosphatidylinositol 3-kinase (PI3K) activity bound to insulin-like growth factor-I (IGF-I) receptor, which is continuously sustained by IGF-I stimulation, is required for IGF-I-induced cell proliferation. Journal of Biological Chemistry, 287(35), 29713-29721.

76.   Cao, Z., Liu, L.Z., Dixon, D.A., et al. (2007). Insulin-like growth factor-I induces cyclooxygenase-2 expression via PI3K, MAPK and PKC signaling pathways in human ovarian cancer cells. Cellular signalling, 19(7), 1542-1553.

77.   Qian, H., Xuan, J., Liu, Y. et al. (2016). Function of G-protein-coupled estrogen receptor-1 in reproductive system tumors. Journal of immunology research, 2016, 7128702.

78.   Revankar, C.M., Cimino, D.F., Sklar, L.A., et al. (2005). A transmembrane intracellular estrogen receptor mediates rapid cell signaling. Science, 307(5715), 1625-1630.

79.   Albanito, L., Madeo, A., Lappano, R., et al. (2007). G protein–coupled receptor 30 (GPR30) mediates gene expression changes and growth response to 17β-estradiol and selective GPR30 ligand G-1 in ovarian cancer cells. Cancer research, 67(4), 1859-1866.

80.   Fujiwara, S., Terai, Y., Kawaguchi, H., et al. (2012). GPR30 regulates the EGFR-Akt cascade and predicts lower survival in patients with ovarian cancer. Journal of ovarian research, 5(1), 1-10.

81.   François, C.M., Wargnier, R., Petit, F., et al. (2015). 17β-estradiol inhibits spreading of metastatic cells from granulosa cell tumors through a non-genomic mechanism involving GPER1. Carcinogenesis, 36(5), 564-573.

82.   Schüler-Toprak, S., Weber, F., Skrzypczak, M., et al. (2018). Estrogen receptor β is associated with expression of cancer associated genes and survival in ovarian cancer. BMc cancer, 18(1), 1-9.

83.   Bookman, M.A., Darcy, K.M., Clarke-Pearson, D., et al. (2003). Evaluation of monoclonal humanized anti-HER2 antibody, trastuzumab, in patients with recurrent or refractory ovarian or primary peritoneal carcinoma with overexpression of HER2: a phase II trial of the Gynecologic Oncology Group. Journal of clinical oncology, 21(2), 283-290.

84.   Thouvenin, L., Charrier, M., Clement, S., et al. (2021). Ovarian cancer with high-level focal ERBB2 amplification responds to trastuzumab and pertuzumab. Gynecologic Oncology Reports, 37, 100787.

85.   Lorusso, D., Hilpert, F., Martin, A.G., et al. (2019). Patient-reported outcomes and final overall survival results from the randomized phase 3 PENELOPE trial evaluating pertuzumab in low tumor human epidermal growth factor receptor 3 (HER3) mRNA-expressing platinum-resistant ovarian  cancer. International Journal of Gynecologic Cancer, 29(7).

86.   Chelariu-Raicu, A., Levenback, C.F., Slomovitz, B.M., et al. (2020). Phase Ib/II study of weekly topotecan and daily gefitinib in patients with platinum resistant ovarian, peritoneal, or fallopian tube cancer. International Journal of Gynecologic Cancer, 30(11).

87.   Schilder, R.J., Pathak, H.B., Lokshin, A.E., et al. (2009). Phase II trial of single agent cetuximab in patients with persistent or recurrent epithelial ovarian or primary peritoneal carcinoma with the potential for dose escalation to rash. Gynecologic oncology, 113(1), 21-27.

88.   Gershenson, D.M., Miller, A., Brady, W.E., et al. (2022). Trametinib versus standard of care in patients with recurrent low-grade serous ovarian cancer (GOG 281/LOGS): an international, randomised, open-label, multicentre, phase 2/3 trial. The Lancet, 399(10324), 541-553.

89.   He, Y., Alejo, S., Venkata, P.P., Johnson, J.D., Loeffel, I., Pratap, U.P., Zou, Y., Lai, Z., Tekmal, R.R., Kost, E.R. and Sareddy, G.R. (2022). Therapeutic targeting of ovarian Cancer stem cells using estrogen receptor Beta agonist. International journal of molecular sciences, 23(13), 7159.

90.   Banerjee, A., Cai, S., Xie, G., Li, N., Bai, X., Lavudi, K., Wang, K., Zhang, X., Zhang, J., Patnaik, S. and Backes, F.J. (2022). A Novel Estrogen Receptor β Agonist Diminishes Ovarian Cancer Stem Cells via Suppressing the Epithelial-to-Mesenchymal Transition. Cancers, 14(9), 2311.

In Review

Suggestions Implemented During the COVID-19 Pandemic to Improve Medical Student Dermatology Exposure and Education

December 4, 2023 HMS Review

“The School of Rome” by Felice Giani. Courtesy National Gallery of Art, Washington.

Scout M. Treadwell [1], Maxwell R. Green [1], Sowmya Ravi [1]
Tulane University School of Medicine, New Orleans, Louisiana, 70112
Correspondence: STreadwell@tulane.edu 


ABSTRACT
The COVID-19 pandemic has drastically changed medical education for both preclinical and clinical students. Virtual learning, shortened rotation schedules, cancelled away rotations, decreased interactions with faculty and mentors, and other curriculum adaptions have had a profound effect on the learning opportunities students receive during their medical training. Research studies surveying US medical schools show students interested in dermatology have limited exposure to this specialty in the medical school curriculum, and the COVID pandemic has exacerbated this lack of clinical experience. This article serves as a review of proposed improvements and current adjustments that have been implemented amid the COVID-19 pandemic in medical schools across the country to address the unforeseen changes in dermatology medical education. General changes have included virtual dermatology electives, student involvement in teledermatology, and online mentorship programs.


INTRODUCTION
Beginning in 2020, the COVID-19 pandemic led to extraordinary disruptions in medical education across the United States. To maintain CDC guidelines and prevent the spread of infection and decrease mortality rates, classes were moved online, instructors were delivering lectures virtually to maintain the integrity of medical student education, and students were deemed nonessential in the hospital on clinical rotations. Shadowing opportunities and clinical rotations for students were limited as minimum room occupancy guidelines were implemented and students were isolated from their peers, faculty, and advisors. In response to this shift of learning, innovative methods to continue to provide patient care and train medical students have been established. 

Dermatological assessment is a vital diagnostic skill in clinical practice. Understanding skin pathologies is significant for all physicians; however, many medical graduates feel they were not adequately exposed to dermatology. Additionally, the COVID-19 pandemic has exacerbated this lack of experience. Patients often present to non-dermatologists who may not know how to diagnose and treat dermatologic conditions. Research has shown that dermatologic diagnoses made by the primary care physician were concordant with that made by the dermatologists only 57% of the time (1). Thus, incorporating strategies to improve clinical education for all future physicians in the diagnosis and management of dermatologic conditions is imperative. Pre-pandemic, dermatology education was limited in the medical school curriculum (2). Research has shown that although dermatologic conditions have a high disease of burden, dermatology is not widely included in medical school curricula (3). Specifically, a study surveying 137 Allopathic US medical schools showed that only sixteen of the 137 schools had a course dedicated to dermatology in the first two preclinical learning years (3). Furthermore, only two of the surveyed schools required a third-year dermatology clinical rotation (3). While most medical schools incorporate dermatology lectures throughout broader educational systemic blocks, students must take the initiative to reach out to mentors in the field, set up shadowing opportunities, and plan for away rotations to establish a relationship with residency programs. During the COVID-19 pandemic, these opportunities have been increasingly limited, and restrictions have made it difficult to learn from experts in this field (4). Without exposure to the field of dermatology throughout the entirety of the one’s medical school education, it may present as challenge for students matching into this field. This review serves a summary of the literature on how the COVID-19 pandemic has led to new innovative teaching methods in a virtual learning environment as well as suggestions on ways to improve student exposure to the field of dermatology.

METHODS
Two of us (S.M.T. and M.R.G.) independently identified studies published in February of 2020 to current date to account for the current pandemic that reported impact of COVID-19 on curriculum and learning changes in United States medical students’ preclinical and clinical dermatology education. We systematically searched PubMed, Embase, and Web of Science were searched in December of 2021 using the terms ("Dermatology education" or "dermatology curriculum" or "dermatology knowledge") and ("medical school" or "medical student").  A total of 892 results were returned from this search. Following Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA), we exported our search results into Covidence for systematic review management (5). After removal of duplicates, full-text articles were obtained if their abstracts were considered eligible by at least 1 of us. Each full-text article was  assessed independently for final inclusion in this systematic review and meta-analysis, and disagreements were resolved by consensus. Studies were included if they met all criteria below.

Inclusion Criteria
1.     COVID-19 on curriculum and learning changes in United States medical students’ preclinical and clinical dermatology education.
2.     Teledermatology in medical education.
3.     Virtual mentoring opportunities for students interested in dermatology.
Results were limited to those published in 2020 to account for published articles on the current pandemic.

Exclusion Criteria
1.     Dermatology residency training during the COVID-19 pandemic.
2.     Dermatological manifestations of COVID-19 infection.

Figure 1: Flow diagram of the literature search using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Adapted from http://prisma-statement.org.

RESULTS
A total of 10 articles were included in this review, see Figure 1. A discussion of the role of the COVID-19 pandemic or its resulting effects on medical student dermatology curriculum and learning is summarized. The characteristics of the included studies are summarized in Table 1.

Table 1: Characteristics of the 10 included studies.

Virtual/Remote Learning
The traditional hands-on clerkship learning environment has been deconstructed due to the virtual changes the COVID-19 pandemic has imposed. To combat these alterations in learning, Patel et al. suggest the addition of a dermatology elective in the medical student curriculum to ensure students are comfortable and competent in the art of history taking, physical examination, documenting, and therapeutic management of common dermatological conditions (4). This proposed elective includes lessons on describing dermatology morphology and understanding the underlying pathophysiology causing the most common dermatology diagnoses. Virtual and online lectures can achieve this mission. Patel et al. also propose a flipped classroom approach that includes video learning so students can learn dermatologic procedures (4). Virtual platforms have been essential in continuing medical student dermatology education during the pandemic. The implementation of prerecorded dermatology lectures into medical student curriculum, proposed by Lipner et al., allows for students to learn at their own pace without the constraints of a socially distanced and limited occupancy classroom (6). Increasing access to virtual dermatology learning models for medical students may improve dermatology education and exposure. 

A piloted virtual 4-week dermatology elective provided students the unique opportunity to participate in virtual patient care was well as learn from faculty. Students were able to collect a history via phone call conversations with patients. Students were also able to describe dermatology morphologies based on the photographs patients submitted. This allowed students to formulate a differential diagnosis and recommend further diagnostic screening tests to their assigned faculty mentor. Students wrote clinical notes on the patients they evaluated as well as gave oral pretentions of the patients they spoke with. Students consulted approximately 100 patients and were exposed to a variety of dermatology conditions including rashes, sarcoidosis or immunotherapy induce skin toxicities (7). Individuals involved in this pilot program received valuable feedback from their faculty mentor on their oral presentation and clinical consulting skills. All students reported a substantial increase in their prior dermatology knowledge and confidence (7). Because COVID-19 limits student presence in dermatology clinics, teledermatology serves a critical role in medical student education. This pilot virtual dermatology elective created by Brigham and Women’s Hospital Department of Dermatology serves as a positive model for other medical schools interesting in increasing student exposure to the field of dermatology amid the international pandemic (7).

Teledermatology
The role of telehealth visits has become increasingly important amid the COVID-19 pandemic. Specifically, many dermatology practices have relied on telemedicine to virtually assess and treat patients. Su et al. states that this shift in care provides a unique opportunity to educate budding dermatologists. They encourage student involvement in teledermatology appointments as this aspect of dermatology will remain an important component of future patient care (8). A teledermatology rotation can provide participating medical students the opportunity to eConsult cases in a self-directed manner which allows for an individual formulation of a differential diagnoses. Medical students are also expected to learn the importance of longitudinally monitoring patients with chronic dermatologic conditions during a global pandemic. The implementation of teledermatology visits expands access to dermatologic care and is complementary to traditional trainee education (8).

The burden of managing both virtual and in-person patients presents a challenge to dermatology practices. Nine Yale School of Medicine students created the Teledermatology Student Task Force aiming to enhance the efficacy of care provided during the pandemic in a dermatology continuity clinic (9). Volunteers of this task force were able to help schedule patient video appointments and upload any clinically important images to patient charts. Students were able to assist in patient education ensuring patients were familiar with operating a smartphone or tablet for the telemedicine visit as well as how to access and open their own patient chart during the appointment. This pilot program was able to contact 104 patients (9). Of the 104, 87 patients were successfully reached by phone by the student task force volunteers and 93% reported that the outreach was helpful (9). 78% of these patients were able to successfully complete a video visit with their dermatologist (9). This study shows the potential for medical students to learn from telemedicine visits as well as the student opportunity to support both physicians and patients leading to optimal utilization of teledermatology.

In 2020, a pediatric dermatology student-run clinic was established to provide dermatologic care to an underserved population whose health care disparities and inequities were widened by the COVID-19 pandemic. Due to pandemic restrictions, care was delivered virtually. Patients were able to submit photographs of their dermatology complaints and three pre-clinical students were able to learn from dermatology resident’s teaching sessions on the specific presentations (10). Students were also able to virtually take patient history under the supervision of the dermatology resident or attending. These interactions increased students’ skills in history taking and improved their basic dermatology knowledge (10). Creating more teledermatology student run clinics can provide students interested in dermatology pre-clinical mentors as well as increasing exposure to the field of dermatology while still delivering care to underserved patients amid a global pandemic.

Away Rotations
Due to program restrictions, away rotations have been limited for visiting medical students. Traditionally, away rotations were critical for developing connections with faculty and obtaining letters of recommendation (2). With these clinical experiences delayed or cancelled, many students are concerned that they will be at a disadvantage for matching into a residency program. Stewart et. al proposes recommendations ensuring the application process is equitable and fair for medical students applying into dermatology. Measures such as virtual didactics and grand rounds may allow student to interact with dermatology faculty (2). Muzumdar et al. also outlines the challenge away rotations in midst of a pandemic presents. They too note that limiting student rotations negatively affects students interested in applying to the highly competitive field of dermatology, specifically those with no home dermatology departments or limited experience in the field. Creative options for students limited to virtual away rotations include virtual lectures and grand rounds sessions, student participation in teledermatology care, and virtually engaging in case-based learnings sessions (11). This provides an opportunity for students to ask questions to current residents and faculty and have experience with a program they may have not had otherwise due to COVID restrictions. 

Mentorship
Mentorship is a critical aspect of a successful medical career. Advice from a supportive mentor can change the trajectory for medical students. According to a study completed by Alikhan et al., 4 students who matched in Dermatology noted that having an encouraging mentor that provided invaluable advice was crucial in a successful match (12). Amidst the COVID-19 pandemic, students are in an unprecedented position in which their access to mentorship is limited. Therefore, it is imperative to improve methods promoting more access to mentorship in dermatology. Minority students have reported that lack of a supportive mentor is an obstacle in applying to a dermatology residency program. In 2018, only 6% of dermatology faculty at medical schools across the United States identified as Black or Latino (13). Creating virtual dermatology mentorship programs can diversify student’s access to mentors. These programs can include alumni directories of clinicians in specific specialties as a resource for students. Web-based open houses hosted by program directors may also provide opportunities for students to establish connections with mentors, especially for students with no home institution (13). While forming a relationship with a mentor in a virtual setting may not develop as organically as an in-person interaction, the goal of a virtual mentoring program can increase access for students to find support within the field of dermatology.

DISCUSSION
In this systematic review of alterations to dermatology teachings in medical education in response to the COVID-19 pandemic, we found suggestions that have been implemented to improve curriculum in areas of virtual and remote learning, teledermatology, away rotations, and mentorship. These proposals can continue to be implemented to further improve medical school dermatology curriculum.

As with many other medical specialties, the field of dermatology has had to adjust to COVID-19 by improving virtual teaching options for students and expanding teledermatology care and education. In addition, the pandemic has limited student access to both away-rotations and mentorship opportunities, both vital components to the traditional undergraduate dermatology education for those interested in the field.

Although many adjustments were made to create virtual medical school curricula, students overall see a benefit in virtual learning for its increased flexibility with their study schedule (14). Additionally, no differences in the effectiveness of online versus offline medical education have been shown (15). These results argue in favor for the benefits of widespread online medical education that took place during the COVID-19 pandemic; however, increased stress and burnout have also been associated with online learning platforms, suggesting that hybrid curricula with both on and offline components may be the most effective for medical students (16). A hybrid plan could include virtual lectures with in-person case-based team learning sessions.

The reduced access to both mentorship and away rotations is more challenging to tackle, but the creation of virtual away rotations may be an effective solution to both these issues. Virtual opportunities in the field of radiology increased student access to both rotation experience and mentorship while lowering the costs associated with traditional away experiences; the largest reported drawbacks were delays for students and technical difficulties (17). Increased access to rotations was also observed with virtual plastic surgery rotations, but students felt that it was difficult to connect and stand out through a virtual platform (18). Although these same challenges faced by other medical specialties with virtual rotations would likely be experienced in dermatology, the benefits of creating more virtual programs to improve access may be worthwhile. Traditional away rotations add an extra cost for medical students and the cost associated may disadvantage students from completing rotations and establishing relationships with non-home programs. Establishing more virtual away rotations will decrease financial burden and may increase opportunities and equity for all students interested in going into dermatology.

CONCLUSIONS
The effects of COVID-19 on undergraduate dermatology education will likely outlast the pandemic itself, so using the lessons learned over the past two years is essential for continuing to strengthen the field of dermatology. The current literature demonstrates improvements and suggestions that have been implemented to account for the rapid classroom changes imposed by the ongoing pandemic. These recommendations have taken into account the importance of continuity in medical student education and have increased exposure for students virtually in a time defined by isolation. Furthermore, as the pandemic continues to evolve, it is crucial to continue to adapt current models to ensure students interested in dermatology have access to knowledge, mentorship, and feel confident applying for a residency position in this field.

DISCLOSURES
Funding: Not applicable.
Conflicts of interest: None.
Availability of data and materials: Not applicable.
Code availability: Not applicable.
Ethics approval: Not applicable.
Consent to participate: Not applicable.

REFERENCES
1.     Lowell, B. A., Froelich, C. W., Federman, D. G., & Kirsner, R. S. (2001). Dermatology in primary care: Prevalence and patient disposition. Journal of the American Academy of Dermatology, 45(2), 250–255.

2.     Stewart, C. R., Chernoff, K. A., Wildman, H. F., & Lipner, S. R. (2020). Recommendations for medical student preparedness and equity for dermatology residency applications during the COVID-19 pandemic. Journal of the American Academy of Dermatology, 83(3), e225–e226.

3.     Cahn, B. A., Harper, H. E., Halverstam, C. P., & Lipoff, J. B. (2020). Current Status of Dermatologic Education in US Medical Schools. JAMA dermatology, 156(4), 468–470.

4.     Patel, P. M., Tsui, C. L., Varma, A., & Levitt, J. (2020). Remote learning for medical student-level dermatology during the COVID-19 pandemic. Journal of the American Academy of Dermatology, 83(6), e469–e470.

5.     Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., McGuinness, L. A., … Moher, D. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ (Clinical research ed.), 372, n71.

6.     Lipner, S. R., Shukla, S., Stewart, C. R., & Behbahani, S. (2021). Reconceptualizing dermatology patient care and education during the COVID-19 pandemic and beyond. International journal of women's dermatology, 7(5), 856–857.

7.     Ashrafzadeh, S., Imadojemu, S. E., Vleugels, R. A., & Buzney, E. A. (2021). Strategies for effective medical student education in dermatology during the COVID-19 pandemic. Journal of the American Academy of Dermatology, 84(1), e33–e34.

8.     Su, M. Y., Lilly, E., Yu, J., & Das, S. (2020). Asynchronous teledermatology in medical education: Lessons from the COVID-19 pandemic. Journal of the American Academy of Dermatology, 83(3), e267–e268.

9.     Belzer, A., Olamiju, B., Antaya, R. J., Odell, I. D., Bia, M., Perkins, S. H., & Cohen, J. M. (2021). A novel medical student initiative to enhance provision of teledermatology in a resident continuity clinic during the COVID-19 pandemic: a pilot study. International journal of dermatology, 60(1), 128–129.

10.   Linggonegoro, D., Rrapi, R., Ashrafzadeh, S., McCormack, L., Bartenstein, D., Hazen, T. J., Kempf, A., Kim, E. J., Moore, K., Sanchez-Flores, X., Song, H., Huang, J. T., & Hussain, S. (2021). Continuing patient care to underserved communities and medical education during the COVID-19 pandemic through a teledermatology student-run clinic. Pediatric dermatology, 38(4), 977–979.

11.   Muzumdar, S., Grant-Kels, J. M., & Feng, H. (2020). Medical student dermatology rotations in the context of COVID-19. Journal of the American Academy of Dermatology, 83(5), 1557–1558.

12.   Alikhan, A., Sivamani, R. K., Mutizwa, M. M., & Aldabagh, B. (2009). Advice for medical students interested in dermatology: perspectives from fourth year students who matched. Dermatology online journal, 15(7), 4.

13.   Fernandez, J. M., Behbahani, S., & Marsch, A. F. (2021). A guide for medical students and trainees to find virtual mentorship in the COVID era and beyond. Journal of the American Academy of Dermatology, 84(5), e245–e248.

14.   Creagh, S., Pigg, N., Gordillo, C., & Banks, J. (2021). Virtual medical student radiology clerkships during the COVID-19 pandemic: Distancing is not a barrier. Clinical imaging, 80, 420–423.

15.   Pei, L., & Wu, H. (2019). Does online learning work better than offline learning in undergraduate medical education? A systematic review and meta-analysis. Medical education online, 24(1), 1666538.

16.   Mheidly, N., Fares, M. Y., & Fares, J. (2020). Coping With Stress and Burnout Associated With Telecommunication and Online Learning. Frontiers in public health, 8, 574969. 

17.   Janopaul-Naylor, J., Qian, D., Khan, M., Brown, S., Lin, J., Syed, Y., Schlafstein, A., Ali, N., Shelton, J., Bradley, J., & Patel, P. (2021). Virtual Away Rotations Increase Access to Radiation Oncology. Practical radiation oncology, 11(5), 325–327.

18.   Tucker, A. B., Pakvasa, M., Shakir, A., Chang, D. W., Reid, R. R., & Silva, A. K. (2022). Plastic Surgery Away Rotations During the Coronavirus Disease Pandemic: A Virtual Experience. Annals of plastic surgery, 88(6), 594–598.

In Review

Dendritic Cell Vaccines in Ovarian Cancer: Have We Reached Their Potential?

December 4, 2023 HMS Review

“The Land-Crab (Cancer ruricola)” by Mark Catesby. Courtesy National Gallery of Art, Washington.

Jennifer Rowley [1]
[1] Harvard Medical School, Boston, Massachusetts 02115
Correspondance: Jennifer_Rowley@hms.harvard.edu


ABSTRACT
Ovarian cancer is the most lethal gynecological malignancy, largely driven by high rates of relapse and chemoresistance. Ovarian cancer is thought to be immunogenic, making it amenable to immunotherapy. However, immunotherapies such as PD-L1 inhibitors and T cell transfers have produced modest, if any, survival benefit. One particular immunotherapy of interest is the dendritic cell vaccine, which delivers mature dendritic cells loaded with tumor antigens with the goal of mounting a T cell response against tumor cells. This review will focus on the role that dendritic cells play in the ovarian tumor microenvironment, general approaches to engineering dendritic cell vaccines and assessing their efficacy alone and in combination with other immunotherapies and systemic chemotherapies. Finally, we will discuss important areas of ongoing research in the field, including the development of personalized neoantigen-targeting DC vaccines.


INTRODUCTION
Ovarian cancer is the most lethal gynecological cancer, with an overall 5-year survival of 48% in 2020. Of those that present at an advanced stage, the 5-year survival is just 29% (1). The current standard treatment of epithelial ovarian cancer (EOC) is debulking surgery followed by platinum-based chemotherapy. Despite good initial responses in most patients, chemoresistance and relapse are common (2, 3). For patients with resistance to platinum-based therapies, their treatment options remain limited.

Ovarian cancer is thought to be immunogenic as it expresses multiple well-known tumor-associated antigens (TAA). Some tumors are infiltrated by lymphocytes, which correlates positively with progression-free survival and overall survival (4, 5). These data suggest that ovarian cancer could be amenable to immunotherapy targeting. However, to date, immunotherapies have shown modest, if any, benefit. For example, PD-1 inhibitors have a response rate of 11.5% in advanced metastatic disease, which is thought in part due to poor T cell infiltration as well as poor antigen presenting function of antigen-presenting cells (APCs) (6, 7).

DENDRITIC CELLS IN OVARIAN CANCER
Dendritic cells in the tumor microenvironment take up and process tumor-associated antigens and present them on MHC I/II molecules to activate CD8+ and CD4+ cells, respectively. In comparison to other APCs such as B cells, mononuclear cells and macrophages, DCs are regarded as the most powerful cell type in its ability to capture, process and present antigens (8). In general, different DCs subtypes can play a multitude of roles in the tumor microenvironment. Conventional DCs (cDC) are the main subtype tasked with activating CD8+ T cells (particularly cDC type 1) and differentiation of CD4+ T cells (cDC type 2) through cytokine production (9). Conversely, plasmacytoid DCs (pDC), which are the main subtype of DCs in ovarian cancer, can exert both anti-tumor and immunosuppressive effects. Whether pDCs skew towards being tumor-protective or tumor-suppressive is largely determined by the signals they receive from their tumor microenvironment (9, 10).

Investigations into ovarian cancer have revealed that dendritic cells (DCs) make essential contributions to the depressed immune function observed in the ovarian tumor microenvironment. While ovarian cancer lesions have a high degree of DC infiltration, these DCs can have low efficacy of antigen presentation due to DC tolerance, which is characterized by downregulated expression of costimulatory molecules on the surface of DC cells and weaker antigen-presenting ability (11). Further, DCs can support the immunosuppressive milieu through their interactions with Tregs. For example, DC expression of indoleamine 2,3-deoxygenase, an essential enzyme in amino acid metabolism, can reduce the amount of tryptophan near Tregs and as a result maintain Tregs in an immunosuppressive state through mTORC-Akt signaling (12). DCs have also been shown to activate immunosuppressive Tregs by expressing ICOS ligand, leading to tumor progression (13).

Vaccines of functional DCs loaded with tumor-associated antigens have held promise for expanding tumor-specific T cell populations by restoring antigen presentation to T cells and bypassing the dysregulated milieu of the tumor microenvironment. There are different types of cancer vaccines, including cell-based vaccines, peptide/protein vaccines, epigenetic vaccines and genetic vaccines (14). This review will focus on the two most common DC vaccine types: cell-based and peptide/protein-based vaccines, which are designed to present T cells with tumor-associated antigens. Specifically, we will review the general approaches to engineering these vaccines as well as assessing their efficacy alone and in combination with other immunotherapies and systemic chemotherapies. Finally, we will discuss important areas of ongoing research in the field, including the development on personalized neoantigen-targeting DC vaccines.

ENGINEERING DENDRITIC CELL VACCINES
Dendritic cell vaccines have long been an immunotherapy of interest for ovarian cancer, particularly given the demonstrated dysfunction of DCs surrounding ovarian tumors. Further, these vaccines are generally well-tolerated by patients and can induce long-term immunologic memory (15).

Currently, vaccines targeting DCs ex vivo are produced using three general steps. First, apheresis is performed to obtain either immune cells that have the potential to become DCs, such as monocytes, or immature DCs from peripheral blood. Among all cell types, monocyte-derived DCs (MoDCs) are most often used as immature DCs are typically not found in sufficient quantity in peripheral blood to produce a vaccine. Monocytes are subsequently cultured in vitro with a cytokine cocktail of GM-CSF and IL-4 that induces differentiation into immature DCs (16). However, from a functional standpoint, MoDCs have been shown to be inferior to cDCs in inducing long-lasting immune responses through T-cell activation, raising questions of their appropriateness as the DC subtype used in many EOC vaccines (17).

Second, once immature DCs have been obtained, they are loaded with tumor-associated antigens, ranging from specific peptides to proteins to multiple antigens from whole tumor lysates. To date, the most common approach has been to load DCs with one or several peptides known to be expressed on ovarian cancer cells. One example includes Wilms tumor 1 (WT-1), which is overexpressed in ovarian cancer along with many other solid tumors and can be targeted by cytotoxic T cells (CTLs). One group incubated DCs with an MHCI-restricted WT-1 peptide and a streptococcal primer and showed that these DCs elicited a CTL effect (18). This has been repeated with other peptides expressed by ovarian cancer cells, such as Her-2/neu, epithelial mucin 1, and p53. These vaccines reproducibly generated antigen-specific IFN-y secreting T cells (19, 20). However, the success of these single peptide/protein vaccines has been limited, resulting in short-term disease stabilization that ultimately gives way to progression after several months. One possible theory for this is that when a vaccine target is a non-mutated self-antigen or shared antigen that is overexpressed in the tumor, vaccine efficacy can be low because T cell recognition of self-antigens will be limited by central tolerance (21). 

More recently, whole tumor cell lysates have been investigated as an antigen source for DC vaccines. In this scheme, DCs are pulsed with lysed ovarian tumor cells. These cells can be derived from ovarian cancer cell lines or even from a patient’s own tissue sample (22). This has the benefit over single peptide vaccines in that lysates can elicit responses to more than one neoantigen, thus reducing avenues for tumor escape. Further, in the case of an autologous tumor cell lysate, the patient can produce a more “personalized” tumor-specific T cell pool by targeting their own unique set of tumor-associated antigens (23, 24). Previous studies with DC vaccines loaded with whole tumor lysate have demonstrated clinical benefit for patients with non-Hodgkin’s lymphoma and melanoma (25, 26). There are multiple approaches to stimulating cell death to induce antigen release, including repetitive freeze-thaw cycles or exposing cells to hypochlorous acid (HOCl). Chiang et al. found that autologous ovarian tumor cells killed with oxidation and lysed with freeze-thaw cycles were superior to cells killed with irradiation or freeze-thaw lysis in priming T cell responses in vitro (27). 

Finally, regardless of the antigen, immature antigen-presenting DCs are then matured in the presence of immunogenic substances like LPS and IFN-y to trigger expression of co-stimulatory molecules on the DC surface. These co-stimulatory molecules are essential for T cell activation upon antigen presentation in the lymph node. Once this step is complete, DCs are typically fractionated into multiple doses to be used as serial vaccines over a defined treatment period. Typically, vaccines are given intranodally, but can also be given with intramuscular or subcutaneous injection (16).

PERSONALIZED NEOANTIGEN-TARGETING VACCINES – THE NEXT BIG LEAP?
One key area of interest for DC vaccines is the development of neoantigen-targeted vaccines. Neoantigens are proteins expressed by tumors that differ from other tumor-associated antigens in several keys. First, neoantigens arise from DNA mutations within the tumor and thus produce peptides that are specific to tumor cells. Because these antigens are not expressed on other cell types in the body, they are often highly immunogenic in comparison to other tumor-associated antigens, which are typically non-mutated self-antigens that are simply overexpressed on cancer cells. This increased immunogenicity is in part driven by increased affinity for major histocompatibility complexes (MHCs) (31). Further, because neoantigens are only expressed by tumor cells, this limits potential off-target effects. Thus, neoantigens are ideal targets for an anti-tumor T cell response (31). While ovarian cancers have lower mutational burdens than most other cancer types, recent analyses have shown that some patients can express moderate to high levels of neoantigens (32). In other cancers, neoantigen-loaded DC vaccines have shown promising results in small phase I trials in melanoma patients and non-small cell lung cancers (33, 34).

Neoantigens can be classified as being shared or personalized. Shared neoantigens are common in some tumor types and can be used to broadly treat patients who have the same tumor type. However, not all patients express these shared neoantigens, and even if they are expressed, different patients may mount different immune responses to them (35). Thus, personalized neoantigens, which are specific to individual patients and tumors, have become a point of interest for DC vaccine design across all cancer types (22). However, identifying a patient’s neoantigen repertoire has only recently become possible with the development of next-generation sequencing (NGS) such as whole exome sequencing, mass spectrometry analysis of the immunopeptidome (i.e., peptides associated with HLAs), as well as highly predictive bioinformatics tools (36-38).

Initial evidence suggests that recognition and targeting of tumor-specific neoantigens improves the effectiveness of dendritic cell vaccines in ovarian cancers. For example, patients with tumor lysate-pulsed DCs were found to have activated high-avidity CD8+ T cell clonal expansion specific for de novo neoantigens, which improved progression-free survival compared to patients without neoantigen-specific T cell responses (29).

However, multiple research groups are still investigating the true benefit of this approach compared to using overexpressed self-antigens or whole tumor lysates, particularly because the process of identifying a patient’s neoantigens remains costly and resource-intensive (22). Currently, one trial is underway to assess a personalized neoantigen-pulsed DC vaccine in ovarian cancer patients. This trial will investigate the feasibility and safety of a personalized neoantigen-loaded DC vaccine in patients with ovarian cancer (ClinicalTrials.gov NCT04024878).

CONCLUSIONS
Dendritic cell vaccines have been shown to be an effective immunotherapy for ovarian cancer, however it remains an active area of innovation in the wake of new technological advances in the realms of NGS and bioinformatics. Further, combinations of various immunomodulatory treatments with DC vaccines will likely be required to capitalize on the immunogenicity of ovarian malignancies and will be a crucial area of clinical investigation going forward.

DISCLOSURES
Funding: Not applicable.
Conflicts of interest: None.
Availability of data and materials: Not applicable.
Code availability: Not applicable.
Ethics approval: Not applicable.
Consent to participate: Not applicable.

REFERENCES
1.     Siegel, R.L., Miller, K.D., Jemal, A. (2020). Cancer statistics. CA: A Cancer Journal for Clinicians, 70(1), 7-30. doi:10.3322/CAAC.21590

2.     Gadducci, A., Cosio, S., Conte, P.F., Genazzani, A.R. (2005). Consolidation and maintenance treatments for patients with advanced epithelial ovarian cancer in complete response after first-line chemotherapy: a review of the literature. Critical reviews in oncology/hematology, 55(2), 153-166. doi:10.1016/J.CRITREVONC.2005.03.003

3.     Stuart, G.C.E. (2003). First-line treatment regimens and the role of consolidation therapy in advanced ovarian cancer. Gynecologic oncology, 90(3 Pt 2), S8. doi:10.1016/S0090-8258(03)00472-4

4.     Vonderheide, R.H., Hahn, W.C., Schultze, J.L., Nadler, L.M. (1999). The telomerase catalytic subunit is a widely expressed tumor-associated antigen recognized by cytotoxic T lymphocytes. Immunity, 10(6), 673-679. doi: 10.1016/s1074-7613(00)80066-7

5.     Sato, E., Olson, S.H., Ahn, J., et al. (2005). Intraepithelial CD8+ tumor-infiltrating lymphocytes and a high CD8+/regulatory T cell ratio are associated with favorable prognosis in ovarian cancer. Proceedings of the National Academy of Sciences of the United States of America, 102(51), 18538-43. doi: 10.1073/pnas.0509182102

6.     Kandalaft, L.E., Odunsi, K., Coukos, G. (2019). Immunotherapy in ovarian cancer: Are we there yet? Journal of Clinical Oncology, 37(27), 2460-2471. doi:10.1200/JCO.19.00508

7.     Varga, A., Piha-Paul, S., Ott, P.A., et al. (2019). Pembrolizumab in patients with programmed death ligand 1–positive advanced ovarian cancer: Analysis of KEYNOTE-028. Gynecologic Oncology, 152(2), 243-250. doi:10.1016/J.YGYNO.2018.11.017

8.     Wculek, S.K., Cueto, F.J., Mujal, A.M., Melero, I., Krummel, M.F., Sancho, D. (2020). Dendritic cells in cancer immunology and immunotherapy. Nature Reviews Immunology, 20(1), 7-24. doi:10.1038/S41577-019-0210-Z

9.     Labidi-Galy, S.I., Sisirak, V., Meeus, P., et al. (2011). Quantitative and functional alterations of plasmacytoid dendritic cells contribute to immune tolerance in ovarian cancer. Cancer Research, 71(16), 5423-5434. doi:10.1158/0008-5472.CAN-11-0367

10.   Labidi-Galy, S.I., Treilleux, I., Goddard-Leon, S., et al. (2012). Plasmacytoid dendritic cells infiltrating ovarian cancer are associated with poor prognosis. OncoImmunology, 1(3), 380-382. doi:10.4161/ONCI.18801

11.   Harimoto, H., Shimizu, M., Nakagawa, Y., et al. (2013). Inactivation of tumor-specific CD8+CTLs by tumor-infiltrating tolerogenic dendritic cells. Immunology and Cell Biology, 91(9), 545-555. doi:10.1038/ICB.2013.38

12.   Munn, D.H. and Mellor, A.L. (2016). IDO in the Tumor Microenvironment: Inflammation, Counter-Regulation, and Tolerance. Trends in Immunology, 37(3), 193-207. doi:10.1016/J.IT.2016.01.002

13.   Conrad, C., Gregorio, J., Wang, Y.H., et al. (2012). Plasmacytoid dendritic cells promote immunosuppression in ovarian cancer via ICOS costimulation of Foxp3+ T-regulatory cells. Cancer Research, 72(20), 5240-5249. doi:10.1158/0008-5472.CAN-12-2271

14.   Lluesma, S.M., Wolfer, A., Harari, A., Kandalaft, L.E. (2016). Cancer Vaccines in Ovarian Cancer: How Can We Improve? Biomedicines, 4(2). doi:10.3390/BIOMEDICINES4020010

15.   Bol, K.F., Schreibelt, G., Gerritsen, W.R., de Vries, I.J.M., Figdor, C.G. (2016). Dendritic cell-based immunotherapy: state of the art and beyond. Clin Cancer Res, 22(8), 1897-1906. doi:10.1158/1078-0432.ccr-15-1399

16.   Zhang, X., He, T., Li, Y., et al. (2021). Dendritic Cell Vaccines in Ovarian Cancer. Frontiers in Immunology, 11, 613773. doi:10.3389/FIMMU.2020.613773/BIBTEX

17.   Zhou, Y., Slone, N., Chrisikos, T.T., et al. (2020). Vaccine efficacy against primary and metastatic cancer with in vitro-generated CD103 + conventional dendritic cells. Journal for ImmunoTherapy of Cancer, 8(1). doi:10.1136/JITC-2019-000474

18.   Zhang, W., Lu, X., Cui, P., et al. (2019). Phase I/II clinical trial of a Wilms’ tumor 1-targeted dendritic cell vaccination-based immunotherapy in patients with advanced cancer. Cancer Immunology, Immunotherapy, 68(1), 121-130. doi:10.1007/S00262-018-2257-2

19.   Chu, C.S., Boyer, J., Schullery, D.S., et al. (2012). Phase I/II randomized trial of dendritic cell vaccination with or without cyclophosphamide for consolidation therapy of advanced ovarian cancer in first or second remission. Cancer Immunology, Immunotherapy, 61(5), 629-641. doi:10.1007/S00262-011-1081-8

20.   Rahma, O.E., Ashtar, E., Czystowska, M., et al. (2012). A gynecologic oncology group phase II trial of two p53 peptide vaccine approaches: Subcutaneous injection and intravenous pulsed dendritic cells in high recurrence risk ovarian cancer patients. Cancer Immunology, Immunotherapy, 61(3), 373-384. doi:10.1007/S00262-011-1100-9

21.   Schietinger, A., Philip, M., Schreiber, H. (2008). Specificity in cancer immunotherapy. Seminars in Immunology, 20(5), 276-285. doi:10.1016/j.smim.2008.07.001

22.   Tang, L., Zhang, R., Zhang, X., Yang, L. (2021). Personalized Neoantigen-Pulsed DC Vaccines: Advances in Clinical Applications. Frontiers in Oncology, 11, 701777. doi:10.3389/FONC.2021.701777/BIBTEX

23.   Hatfield, P., Merrick, A.E., West, E., et al. (2008). Optimization of dendritic cell loading with tumor cell lysates for cancer immunotherapy. Journal of Immunotherapy, 31(7), 620-32. doi: 10.1097/CJI.0b013e31818213df

24.   Cho, D., Yang, W., Lee, H., et al. (2012). Adjuvant immunotherapy with whole-cell lysate dendritic cells vaccine for glioblastoma multiforme: a phase II clinical trial. World Neurosurgery, 77(5-6), 736-44. doi: 10.1016/j.wneu.2011.08.020

25.   Di Nicola, M., Zappasodi, R., Carmelo, C.S., et al. (2009). Vaccination with autologous tumor-loaded dendritic cells induces clinical and immunologic responses in indolent B-cell lymphoma patients with relapsed and measurable disease: a pilot study. Blood, 113(1), 18-27. doi:10.1182/blood-2008-06-165654

26.   Palucka, A.K., Ueno, H., Connolly, J., et al. (2006). Dendritic cells loaded with killed allogeneic melanoma cells can induce objective clinical responses and MART-1 specific CD8 + T-cell immunity. J Immunother, 29(5), 545-557. doi:10.1097/01.cji.0000211309.90621.8b

27.   Chiang, C.L.L., Kandalaft, L.E., Tanyi, J., et al. (2013). A dendritic cell vaccine pulsed with autologous hypochlorous acid-oxidized ovarian cancer lysate primes effective broad antitumor immunity: from bench to bedside. Clinical cancer research : an official journal of the American Association for Cancer Research, 19(17), 4801-4815. doi:10.1158/1078-0432.CCR-13-1185

28.   Kobayashi, M., Chiba, A., Izawa, H., et al. (2014). The feasibility and clinical effects of dendritic cell-based immunotherapy targeting synthesized peptides for recurrent ovarian cancer. Journal of ovarian research, 7(1). doi:10.1186/1757-2215-7-48

29.   Tanyi, J.L., Bobisse, S., Ophir, E., et al. (2018). Personalized cancer vaccine effectively mobilizes antitumor T cell immunity in ovarian cancer. Science translational medicine, 10(436). doi:10.1126/SCITRANSLMED.AAO5931

30.   Kandalaft, L.E., Powell, D.J., Chiang, C.L., et al. (2013). Autologous lysate-pulsed dendritic cell vaccination followed by adoptive transfer of vaccine-primed ex vivo co-stimulated t cells in recurrent ovarian cancer. OncoImmunology, 2(1). doi:10.4161/ONCI.22664

31.   Lawrence, M.S., Stojanov, P., Polak, P., et al. (2013). Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature, 499(7457), 214-218. doi:10.1038/nature12213

32.   O’Donnell, T., Christie, E.L., Ahuja, A., et al. (2018). Chemotherapy weakly contributes to predicted neoantigen expression in ovarian cancer. BMC Cancer, 18(1), 87. doi:10.1186/s12885-017-3825-0

33.   Ding, Z., Li, Q., Zhang, R., et al. (2021). Personalized neoantigen pulsed dendritic cell vaccine for advanced lung cancer. Signal Transduction and Targeted Therapy, 6(1). doi:10.1038/S41392-020-00448-5

34.   Carreno, B.M., Magrini, V., Becker-Hapak, M., et al. (2015). A dendritic cell vaccine increases the breadth and diversity of melanoma neoantigen-specific T cells. Science, 348(6236), 803-808. doi:10.1126/SCIENCE.AAA3828

35.   Mullard, A. (2016). The cancer vaccine resurgence. Nature Reviews Drug Discovery, 15(10), 663-665. doi:10.1038/NRD.2016.201

36.   Boisguérin, V., Castle, J.C., Loewer, M., et al. (2014). Translation of genomics-guided RNA-based personalised cancer vaccines: towards the bedside. British Journal of Cancer, 111(8), 1469-1475. doi:10.1038/bjc.2013.820

37.   Bassani-Sternberg, M., Gfeller, D. (2016). Unsupervised HLA peptidome deconvolution improves ligand prediction accuracy and predicts cooperative effects in peptide-HLA interactions. Journal of Immunology, 197(6), 2492-2499. doi:10.4049/jimmunol.1600808

38.   Gfeller, D., Guillaume, P., Michaux, J., et al. (2018). The length distribution and multiple specificity of naturally presented HLA-I ligands. Journal of Immunology, 201(12), 3705-3716. doi:10.4049/jimmunol.1800914

In Review

Explore More

Featured
Arts
Nov 26, 2024
Forgotten Fragments
Arts
Nov 26, 2024
Read More →
Arts
Nov 26, 2024
Arts
Jul 29, 2024
A calligram in ancient Greek of the Hippocratic Oath taking the shape of the Rod of Asclepius, oil on canvas
Arts
Jul 29, 2024
Read More →
Arts
Jul 29, 2024
Research
Jul 29, 2024
Assessing the Oral Health of the Homeless Population in Central Massachusetts
Research
Jul 29, 2024
Read More →
Research
Jul 29, 2024
Research
Jul 29, 2024
Teaching Medical Spanish Alongside the Medical History: Evaluation of a Decade-Old Peer-Led Medical Spanish Program
Research
Jul 29, 2024
Read More →
Research
Jul 29, 2024
Review
Jul 29, 2024
History, Recent Advances, and Ethical Controversies of Solid Organ Xenotransplantation: Review and Implications for Future Clinical Trials
Review
Jul 29, 2024
Read More →
Review
Jul 29, 2024
Perspective
Jul 29, 2024
Patient Discharge Decision Flowchart: Streamlining Disposition Management after Acute Hospital Stays
Perspective
Jul 29, 2024
Read More →
Perspective
Jul 29, 2024
Perspective
Jul 29, 2024
Doubly Dangerous: Medical Students’ Observations of Weight Bias in the Clinical Setting
Perspective
Jul 29, 2024
Read More →
Perspective
Jul 29, 2024
Review
Jul 29, 2024
Emerging Relationships of Sarcomeric Mutations and the Cardiomyocyte Transcriptome in the setting of Familial Hypertrophic Cardiomyopathy
Review
Jul 29, 2024
Read More →
Review
Jul 29, 2024
Review
Jul 29, 2024
Neuro-Immune Crosstalk: The Relationship Between Adrenergic Stimulation and Macrophages in Developing Upstream Risk Factors for Cardiovascular Disease
Review
Jul 29, 2024
Read More →
Review
Jul 29, 2024
ARCHIVE

HMSR  |  Latest Issue  |  Archive  |  Submit