In this issue, Peres et al. look at survival after epithelial ovarian cancer (EOC) in relation to its histologic subtypes, grade, and stage using data from Surveillance, Epidemiology, and End Results (SEER) (1). For distant-stage disease, mucinous and clear cell carcinoma had higher mortality than high-grade (grades 2–4) serous carcinoma and high-grade (grades 3 and 4) endometrioid carcinoma. (Here “distant” as defined by SEER included International Federation of Gynecology and Obstetrics stage III—spread outside of the pelvis.) Regardless of stage, the worst survival was observed for carcinosarcoma, and the most favorable outcomes occurred with low-grade (grade 1) serous and low-grade (grades 1 and 2) endometrioid. In their primary analysis, carcinoma not-otherwise-specified (NOS) and carcinomas with mixed patterns were excluded. Supplemental analyses found that carcinoma NOS generally tracked with carcinosarcoma, and mixed epithelial types tracked best with clear cell for localized disease and high-grade serous for distant disease. These observations re-affirm the conclusion of many others that EOC should be recognized as a set of distinct diseases rather than a single entity. EOC is not unique where survival differs by histologic subtype. Endometrial cancer comprises nearly identical histologic subtypes with similar profiles of survival from best to worst: endometrioid > papillary serous and clear cell > carcinosarcomas (2,3). Mucinous tumors of the endometrium also exist but are rare, and data on survival are sparse (4). Though not analogous to their counterparts in the ovary and uterus, papillary and clear cell tumors and tumors with a sarcomatous component also exist for renal tumors, with decreasing survival in that order. Generalizations about the behavior of similar histologic subtypes in carcinomas from different organs might be a useful exercise, provided there is good reproducibility among pathologists in diagnosing common subtypes. Aiming for greater reproducibility, the World Health Organization has revised its classification of EOC several times, most recently in 2014 (5). Five histologic subtypes are distinguished; high- and low-grade serous, endometrioid, clear cell, and mucinous, to which Peres et al. added carcinosarcomas and Brenner tumors (1). To some extent, reproducibility is addressed in Table 2 of their article, where the proportions of these types by SEER sites—grouped into Northeast, Northwest, Southeast, and Southwest regions—are examined. Recalculating the proportions of tumor subtypes by row reveals the following ranges: high-grade serous (62.1%–68.9%), low-grade serous (0.6%–3.4%), endometrioid (8.1%–10.3%), clear cell (6.7%–11.8%), mucinous (6.4%–10.5%), carcinosarcoma (4.0%–5.7%), and Brenner (0%–0.3%). It is likely that much of the variation is due to age and race differences in populations from these regions. A formal assessment of reproducibility in five Canadian centers reported Kappa values of 0.80 to 0.97 (average = 0.89) across the five cell types plus an “other” category (6). Peres et al. decided to lump high-grade endometrioid with high-grade serous carcinoma. Pathologists who disagree may point to the infrequency of endometriosis found with serous cancer but common with endometrioid. Conversely, support for the decision comes from slide reviews and biomarker studies. A comprehensive review of slides from EOC cases in British Columbia found that about a quarter of cases originally called endometrioid were reclassified as high-grade serous, the majority of which were high-grade and stained positive for a serous immunohistochemical (IHC) marker, WT-1 (7). Others have shown that the expression patterns of p53, PTEN, and PAX2 unify high-grade serous, high-grade mixed endometrioid and serous, and high-grade endometrioid carcinomas (8). Other IHC markers used in classifying EOC include PAX8 (for most serous and endometrioid carcinomas), Napsin A (for clear cell carcinomas), Stathmin 1 (for serous carcinomas), and CEACAM6 (for mucinous carcinomas) (9–13). In addition, there is increasing use of genomic classification of tumors, for example, loss of ARID1A for clear cell tumors (14). Currently SEER data do not capture information about adjunctive IHC or genomic markers increasingly used in surgical pathology laboratories. To these Editorial writers, the most important dialogue suggested by the Peres et al. study (1) is how SEER can evolve in the coming years to better serve the needs of cancer researchers. An electronic record of the complete pathology report and digitalized photomicrographs of the tumor would capture information about adjunctive IHC markers, permit studies of reproducibility, and likely reduce the number of tumors classified as NOS or mixed. In addition, many cancer centers now routinely perform molecular phenotyping based on tumor DNA or search for heritable mutations in germ-line DNA. Because of legal authority for registries to capture information about cancer, considerable leeway exists in which to add such information and make it available to researchers, as long as de-identifiability of patients is maintained. More details on initial and subsequent treatment should be added to the SEER database. As Peres et al. note, the most important prognostic treatment factors for patients are the extent of surgical cytoreduction and the response to platinum-based chemotherapy (15,16). However, neither of these is currently in the SEER database. Patients should be classified not only by whether surgery was undertaken but also by the adequacy of the surgical resection, the course of neoadjuvant or adjuvant chemotherapy, and the disease-free interval after primary treatment. Apart from enhancing the quality and quantity of data collected by SEER, creation of local or national tissue repositories suitable for genomic analysis would immensely enhance novel research on the molecular pathogenesis of cancer. SEER has already facilitated studies using tissue blocks recovered from registries including one on the prevalence of human papilloma virus in cervical tissues (17). Expanding the SEER infrastructure to build a centralized system able to link currently available SEER data with a multitude of tools and bioinformatics resources would dramatically enhance cancer research. Several recent publications, with input from SEER leadership, reveal that the blueprints for this expansion are already in place, including the American Joint Committee on Cancer Manual on Cancer Staging, 8th edition (18), Oncology Information—Technology to Improve Processes and Outcomes in Cancer (19), and a paper whose title we paraphrased for this Editorial (20). These types of efforts envisioned are clearly necessary to move studies using the SEER database from simple descriptive reports to actionable insights on ovarian and other cancers. Notes Affiliations of authors: Brigham and Women’s Hospital, Department of Obstetrics, Gynecology and Reproductive Biology (DWC), and Gynecologic Oncology Laboratory (KME), Boston, MA. The authors have no conflicts of interest to disclose. The authors wish to acknowledge the advice and suggestions of Dr. Lynne Penberthy of the Surveillance, Epidemiology, and End Results Program at the Surveillance Research Program, Division of Cancer Control and Populations Sciences, National Cancer Institute. References 1 Peres LC , Cushing-Haugen KL , Köbel M et al. , Invasive epithelial ovarian cancer survival by histotype and disease stage . J Natl Cancer Inst . 2019 ; 111 ( 1 ): djy071 . 2 Hamilton CA , Cheung MK , Osann K et al. , Uterine papillary serous and clear cell carcinomas predict for poorer survival compared to grade 3 endometrioid corpus cancers . Br J Cancer. 2006 ; 94 ( 5 ): 642 – 646 . Google Scholar CrossRef Search ADS PubMed 3 Bansal N , Herzog TJ , Seshan VE et al. , Uterine carcinosarcomas and grade 3 endometrioid cancers: Evidence for distinct tumor behavior . Obstet Gynecol. 2008 ; 112 ( 1 ): 64 – 70 . Google Scholar CrossRef Search ADS PubMed 4 Rauh-Hain JA , Vargas RJ , Clemmer J et al. , Mucinous adenocarcinoma of the endometrium compared with endometrioid endometrial cancer: A SEER analysis. Am J Clin Oncol. 2016 ; 39 ( 1 ): 43 – 48 . Google Scholar CrossRef Search ADS 5 Kurman RJ , Carcangiu ML , Herrington CS et al. , WHO Classification of Tumours of Female Reproductive Organs . 4th ed . Lyon, France: IARC Press; 2014 . 6 Kobel M , Kalloger SE , Baker PM et al. , Diagnosis of ovarian carcinoma cell type is highly reproducible: A transcanadian study . Am J Surg Pathol. 2010 ; 34 ( 7 ): 984 – 993 . Google Scholar CrossRef Search ADS PubMed 7 Gilks CB , Ionescu DN , Kalloger SE et al. , Tumor cell type can be reproducibly diagnosed and is of independent prognostic significance in patients with maximally debulked ovarian carcinoma . Hum Pathol. 2008 ; 39 ( 8 ): 1239 – 1251 . Google Scholar CrossRef Search ADS PubMed 8 Roh MH , Yassin Y , Miron A et al. , High-grade fimbrial-ovarian carcinomas are unified by altered p53, PTEN and PAX2 expression . Mod Pathol. 2010 ; 23 ( 10 ): 1316 – 1324 . Google Scholar CrossRef Search ADS PubMed 9 Howitt BE , Emori MM , Drapkin R et al. , GATA3 is a sensitive and specific marker of benign and malignant mesonephric lesions in the lower female genital tract . Am J Surg Pathol. 2015 ; 39 ( 10 ): 1411 – 1419 . Google Scholar CrossRef Search ADS PubMed 10 Karst AM , Levanon K , Duraisamy S et al. , Stathmin 1, a marker of PI3K pathway activation and regulator of microtubule dynamics, is expressed in early pelvic serous carcinomas . Gynecol Oncol. 2011 ; 123 ( 1 ): 5 – 12 . Google Scholar CrossRef Search ADS PubMed 11 Laury AR , Perets R , Piao H et al. , A comprehensive analysis of PAX8 expression in human epithelial tumors . Am J Surg Pathol. 2011 ; 35 ( 6 ): 816 – 826 . Google Scholar CrossRef Search ADS PubMed 12 Lim D , Ip PP , Cheung AN et al. , Immunohistochemical comparison of ovarian and uterine endometrioid carcinoma, endometrioid carcinoma with clear cell change, and clear cell carcinoma . Am J Surg Pathol. 2015 ; 39 ( 8 ): 1061 – 1069 . Google Scholar CrossRef Search ADS PubMed 13 Litkouhi B , Litkouhi B , Fleming E et al. , Overexpression of CEACAM6 in borderline and invasive mucinous ovarian neoplasms . Gynecol Oncol. 2008 ; 109 ( 2 ): 234 – 239 . Google Scholar CrossRef Search ADS PubMed 14 Wiegand KC , Shah SP , Al-Agha OM et al. , ARID1A mutations in endometriosis-associated ovarian carcinomas . N Engl J Med. 2010 ; 363 ( 16 ): 1532 – 1543 . Google Scholar CrossRef Search ADS PubMed 15 Chang SJ , Hodeib M , Chang J et al. , Survival impact of complete cytoreduction to no gross residual disease for advanced-stage ovarian cancer: A meta-analysis . Gynecol Oncol. 2013 ; 130 ( 3 ): 493 – 498 . Google Scholar CrossRef Search ADS PubMed 16 Bowtell DD , Bohm S , Ahmed AA et al. , Rethinking ovarian cancer II: Reducing mortality from high-grade serous ovarian cancer . Nat Rev Cancer. 2015 ; 15 ( 11 ): 668 – 679 . Google Scholar CrossRef Search ADS PubMed 17 Hopenhayn C , Christian A , Christian WJ et al. , Prevalence of human papillomavirus types in invasive cervical cancers from 7 US cancer registries before vaccine introduction . J Low Genit Tract Dis. 2014 ; 18 ( 2 ): 182 – 189 . Google Scholar CrossRef Search ADS PubMed 18 Amin MB, Edge SB, Greene FL, et al. (eds). AJCC Cancer Staging Manual 8th ed. New York: Springer; 2017. 19 Penberthy LT, Winn DM, Scott SM. Chapter 14 - Cancer Surveillance Informatics, In: Hesse BW, Ahern DK and Beckjord E, eds. Oncology Informatics. Boston: Academic Press; 2016:277–285. 20 Altekruse SF , Rosenfeld GE , Carrick DM et al. , SEER cancer registry biospecimen research: Yesterday and tomorrow . Cancer Epidemiol Biomarkers Prev. 2014 ; 23 ( 12 ): 2681 – 2687 . Google Scholar CrossRef Search ADS PubMed © The Author(s) 2018. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: firstname.lastname@example.org. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices)
JNCI: Journal of the National Cancer Institute – Oxford University Press
Published: Apr 28, 2018
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera