Searching for Cancer Information on the Internet : Analyzing Natural Language Search Queries Background Searching for health information is one of the most-common tasks performed by Internet users . Many users begin searching on popular search engines rather than on prominent health information sites . We know that many visitors to our -LRB- National Cancer Institute -RRB- Web site , cancer.gov , arrive via links in search engine result . Introduction For members of the general public who use the Internet , many seek medical information -LSB- 1 - 6 -RSB- . According to a recent systematic review of 24 peer-reviewed publications describing the proportions of Internet users among various populations of cancer patients in the developed world , about 39 % of cancer patients are using the Internet directly , and in addition , 15 % to 20 % of persons with cancer use the Internet `` indirectly '' through family and friends -LSB- 7 -RSB- . Studies have evaluated information-seeking behavior on the Internet by cancer patients generally -LSB- 8 - 10 -RSB- , their companions -LSB- 11,12 -RSB- , and patients with the following common specific cancer diagnoses : breast -LSB- 13 - 16 -RSB- , prostate -LSB- 17,18 -RSB- , lung -LSB- 19 -RSB- , and gastrointestinal cancers -LSB- 20 -RSB- . Studies have also evaluated information gathering by cancer patients undergoing radiotherapy -LSB- 21 -RSB- and chemotherapy -LSB- 22 -RSB- , and those from centers outside of North America -LSB- 23,24 -RSB- . Individuals from certain disadvantaged groups have been shown to seek medical information online less frequently and with more difficulty -LSB- 7,25,26 -RSB- . Eysenbach and Kohler found that general consumers search for medical content using search engines rather than medical portals or sites of medical societies or libraries -LSB- 27 -RSB- . Newly-diagnosed cancer patients and their families often start their searches as users less sophisticated in Web and medical terminology . They too commonly begin searching on popular search engines rather than on prominent cancer-information sites . We know that many visitors to our own Web site -LSB- 28 -RSB- arrive via search engine result links . To better understand users ' needs this research aimed to establish what lay users really want to know when they search online for cancer information . To do this we evaluated data from Ask.com -LSB- 29 -RSB- , a popular natural-language-processing -LRB- NLP -RRB- search engine . Natural-language-processing search engines allow users to create queries using whole phrases and sentences of any length , rather than just key words . Earlier reports of this project have been published in abstract form only . The abstracts reported a brief project summary -LSB- 30 -RSB- , and data specific for breast cancer -LSB- 31 -RSB- and gastrointestinal cancer -LSB- 20 -RSB- . This is the first comprehensive report of the entire project . Methods The National Cancer Institute -LRB- NCI -RRB- partnered with AskJeeves , Inc to develop a methodology to capture , sample , and analyze 3 months of cancer-related queries on the Ask.com Web site , a prominent US natural-language-processing consumer search engine . At the time of the project , Ask.com was receiving over 35 million queries per month . Search Terms An NCI oncologist -LRB- JLB -RRB- developed a benchmark set of 500 terms and word roots that were matched against actual AskJeeves user queries . Most terms and word roots were from the NCI dictionary on the NCI Web site -LSB- 32 -RSB- . NCI also suggested additional terms not included in the dictionary . These terms related to anatomy , organ systems , treatments , pharmaceuticals , treatment and diagnostic procedures , genetics , epidemiology , and pathology . Table 1 Top 37 search terms and roots with ≥ 5 queries per week during test week Term Actual Queries During Test Week % of Total Queries * cancer * 9765 56.75 * tumor * 1396 8.11 * carcino * 656 3.81 * leukemia * 635 3.69 lymphom * 419 2.43 chemotherapy 378 2.20 biopsy/biopsies 375 2.18 * melano * 348 2.02 * sarcoma * 294 1.71 * dysplasia * 255 1.48 hodgkin * 245 1.42 MRI 214 1.24 clinical trial 187 1.09 mammogr 175 1.02 maligna * 170 0.99 * metasta * 155 0.90 '' * '' is a placeholder for the part of the search term before or after the root . The test sample of these 500 words and roots was used to filter cancer queries from the Ask.com Web site for 1 week in August 2001 . From these 500 terms , only 37 appeared ≥ 5 times per day over the trial week . The list of 37 terms -LRB- plus common misspellings -RRB- yielded 17208 queries for the test week . The frequency of each term is shown in Table 1 . Queries with common misspellings , -LRB- eg , prostate and prostrate , biopsy and biopsey , leukemia and lukemia , chemotherapy and chemothereapy -RRB- were captured and analyzed . It was felt that the cut off of 5 times per day -LRB- ≥ 35 times per week -RRB- would capture the key queries and include any common query topic , since even with a frequency of 35 queries a week , the majority of these terms accounted for less than 1 % of the total population . Of the original 500 terms supplied by NCI , only 7 % -LRB- 35/500 -RRB- appeared in the logs at a high frequency , but this 7 % accounts for over 37 % of user queries identified as cancer related on Ask.com during the study period . Collecting Queries and Sampling The process used for collecting and sampling queries is outlined in Figure 1 . Using the 37 terms to search the Ask.com query logs , 204165 instances of cancer-related queries were found for June , July , and August 2001 . Of these queries , 7500 individual user questions were randomly selected by AskJeeves for detailed analysis -LRB- see Appendix 1 -RRB- . Figure 1 Processing of cancer queries on Ask.com Very often there were multiples of the same questions . Thus , these 7500 queries actually represented 76077 queries that were entered into Ask.com , about 37 % -LRB- 76077/204164 -RRB- of all queries identified as cancer-related from the 3 month period of log analysis . For example , a user question might be `` Where can I find information about breast cancer ? '' This individual example represents 1 user question , but might have been queried by more than 100 people on any given day . Each query was counted only once . Sampling Issues The random sample of 7500 individual queries provides a confidence interval of 1.11 % at a confidence level of 95 % . This means that even if more samples were taken from 204165 queries , 95 % of those samples should not be off by more than 1.1 % . While this means that the samples themselves would not vary more than 1.1 % over 95 % of the samples taken , as the data are categorized and classified , in effect smaller and smaller samples are taken . Therefore , to offset this problem additional queries were examined , even though a smaller sample would still provide a high degree of confidence in the results . In other words , although broad generalizations -- such as `` breast cancer accounts for 25 % of all cancer queries '' -- can be easily presented , a large sample size is required to break down data far enough to conclude that when users ask about breast cancer , they are most often asking about specific types of treatments . Highest-Level Categories for Queries User queries were assigned to a set of 6 highest-level categories -LRB- as shown in Table 2 -RRB- : Cancer -LRB- ie , specifically mentioning a cancer type -RRB- General Research Treatment Diagnosis and Testing Cause/Risk/Link Coping Table 2 Highest-level categories for queries Highest-Level Category Number of Queries Percent of All Sampled Queries * Cancer † 59619 78.37 General Research 7808 10.26 Treatment 3832 5.04 Diagnosis and Testing 3315 4.36 Cause/Risk/Link 1249 1.64 Coping 254 0.33 Total 76077 * Percentages do not sum to 100 % due to rounding . † ie , specifically mentioning a cancer type . Highest-level categories were created in a collaborative effort between the AskJeeves data-analysis team and NCI staff before the study period began , but the final category titles were revised as the actual queries were analyzed . The initial categories were based on user queries entered into Ask.com and a variety of online sources , such as NCI 's online dictionary -LSB- 32 -RSB- and NCI 's Physician Data Query -LRB- PDQ -RRB- -LSB- 33 -RSB- . The highest-level categories were populated using proprietary AskJeeves filters and automated-analysis tools that sorted queries according to specific types of cancers , or -- in the absence of mentioning a specific cancer type -- whether the query asked about other areas such as Treatment or Coping . -LRB- AskJeeves did not share the filters and automated analysis tools with the authors . -RRB- Queries that could not be sorted by the filters and automated-analysis tools were placed in a temporarily-uncategorized category ; they were categorized during the next step -LRB- reading and analysis -RRB- . Reading and analyzing each individual query not only verified the automated process , but also helped to refine existing categories and create new categories and subcategories , as appropriate . For example , without this type of analysis , the query `` Where can I find a Web site with information on using high protein food to fight Breast cancer ? '' might have been left under Breast Cancer > Media and Organizations > Web sites -LRB- where '' > '' indicates a change in category level -RRB- . This would not be correct , as the true user intent was to inquire about Alternative Treatments . As a result , under the category Breast Cancer > Treatment , `` Alternative '' was added to the Breast Cancer > Treatment category analysis as a subtopic . -LRB- Treatment -- without a specific cancer site designated -- is both a highest-level category and a subcategory under Breast Cancer and under most cancer types . -RRB- Approximately 78 % of all categorized queries from the sample referenced a particular type of Cancer , and were placed in the highest-level category Cancer . An example of this kind of query would be `` Where can I find information about Breast Cancer ? '' -LRB- This query would be classified as Cancer > Breast Cancer > General Information . -RRB- Any query that did not mention a specific kind of Cancer , even though the question was about cancer , was placed on 1 of the 5 other highest-level categories . An example of this type of query would be `` Where can I find information on cancer treatment with radiation ? '' This query was assigned to the Radiation subcategory in the highest-level category Treatment -LRB- ie , it was classified as Treatment > Radiation -RRB- . Queries that did not relate to a specific Cancer type were placed in 1 of the 5 other highest-level categories : General Research , Treatment , Diagnosis and Testing , Cause/Risk/Link , or Coping . For example the query `` How does smoking cause cancer ? '' would be placed in the Cause/Risk/Link category , as it did not refer to any specific type of cancer . `` Cancer '' Queries -LRB- Related to Specific Cancer Types -RRB- As shown in Table 3 , there were 14 cancer types -LRB- N = 59619 queries -RRB- selected as subcategories of the Cancer highest-level category . For cancer types with the most-frequent queries , like Digestive/Gastrointestinal/Bowel -LRB- D/G/B -RRB- , Breast , and Genitourinary , there were enough queries to populate standard subcategories like General Information , Treatment , Symptoms , Diagnosis and Testing , and Cause/Risk/Link . These common cancer types often warranted the creation of customized subcategories , like Breast > Media and Organizations > Web sites . For the less-common cancer type queries , like Bile -LRB- duct -RRB- in D/G/B , few queries were received and only those in General Information are shown . Table 3 Cancer types Type within Top-Level Cancer Category Number of Queries % Queries in Cancer Category * † % Queries in This Report * ‡ Digestive/Gastrointestinal/Bowel -LRB- D/G/B -RRB- 8959 15.0 11.8 Breast 6953 11.7 9.1 Skin 6709 11.3 8.8 Genitourinary 6250 10.5 8.2 Hematologic/Blood 5448 9.2 7.2 Gynecological 5344 9.0 7.0 Lung 4630 7.8 6.1 Soft Tissue/Muscle 3954 6.6 5.2 Lymphoma 3333 5.6 4.4 Head and Neck 2522 4.2 3.3 Brain/Neurological 1852 3.1 2.4 Miscellaneous Cancer 1633 2.7 2.1 Bone 1429 2.4 1.9 Pediatric 603 1.0 0.8 * Percentages do not sum to 100 % due to rounding . † Denominator -LRB- N = 59619 -RRB- was the total number of queries about specific types in the Cancer category . ‡ Denominator -LRB- N = 76077 -RRB- was the total number of queries analyzed in this report . Privacy Issues Although NCI helped create the search terms and the categories into which the analyzed data was placed , NCI did not have access to : the raw query logs at AskJeeves , any information about what AskJeeves users did with the searches generated on the AskJeeves Web site -LRB- ie , what links they picked -RRB- , or the identities of any users of the Ask.com Web site . NCI did not require permission from the Institutional Review Board . Results Frequency of Top-Level Categories As shown in Table 2 , The 6 highest-level categories in order of decreasing frequency of queries were : Cancer -LRB- N = 59619 , 78.37 % -RRB- General Research -LRB- N = 7808 , 10.26 % -RRB- Treatment -LRB- N = 3832 , 5.04 % -RRB- Diagnosis and Testing -LRB- N = 3315 , 4.36 % -RRB- Cause/Risk/Link -LRB- N = 1249 , 1.64 % -RRB- Coping -LRB- N = 254 , 0.33 % -RRB- The data in Table 2 indicate that the great majority of users asked for information about specific types of cancers , but rarely asked about a Treatment option or Diagnosis and Testing procedure without specifying the particular cancer about which they were concerned . Similarly , users asked few queries about general Symptoms of cancer unrelated to a specific type of cancer -LRB- see Diagnosis and Testing > Symptoms , N = 473 , 14.27 % -RRB- . An example would be `` what are some symptoms of cancer ? '' Subdividing Cancer Queries Table 3 breaks down the highest-level category Cancer queries -LRB- N = 59619 -RRB- into more specific cancer types . In order of decreasing frequency within the Cancer category , the 14 subcategories were : Digestive/Gastrointestinal/Bowel -LRB- D/G/B -RRB- -LRB- N = 8959 , 15.0 % -RRB- Breast -LRB- N = 6953 , 11.7 % -RRB- Skin -LRB- N = 6709 , 11.3 % -RRB- Genitourinary -LRB- N = 6250 , 10.5 % -RRB- Hematologic/Blood -LRB- N = 5448 , 9.2 % -RRB- Gynecological -LRB- N = 5344 , 9.0 % -RRB- Lung -LRB- N = 4630 , 7.8 % -RRB- Soft Tissue/Muscle -LRB- N = 3954 , 6.6 % -RRB- Lymphoma -LRB- N = 3333 , 5.6 % -RRB- Head and Neck -LRB- N = 2522 , 4.2 % -RRB- Brain and Neurological -LRB- N = 1852 , 3.1 % -RRB- Miscellaneous -LRB- N = 1633 , 2.7 % -RRB- Bone -LRB- N = 1429 , 2.4 % -RRB- Pediatric -LRB- N = 603 , 1.0 % -RRB- Any query specifically mentioning a cancer type by name , was assigned to that subcategory . For example , questions about Breast-Cancer-specific Treatment , Diagnosis and Testing , Causes , and Coping are found in the Cancer > Breast Cancer category , within 1 of the 10 subcategories displaying Breast Cancer information . All questions about Leukemia or Myeloma would be found in Hematologic/Blood , Hodgkin 's Disease queries in Lymphoma , and Esophageal cancer questions in D/G/B . The number of subcategories assigned to each of the 14 different cancer types varied somewhat and was driven by the nature and number of the specific queries in those cancer types . Detailed Analysis of Queries The detailed categorizations and verbatim display of examples of sampled queries are shown in Appendix 1 . There is a breakdown of all the 14 cancer types within the highest-level category Cancer as well as a breakdown of queries within the 5 other highest-level categories not referencing any particular cancer type . These 19 are arranged alphabetically in the Appendix . Major observations about the 19 categories and subcategories are noted below , in the order they appear in the Appendix . Our comments emphasize issues related to requested cancer content more than technology issues related to the natural language processing . 1.0 Bone Cancer As shown in Appendix 1 , there were 1429 queries about Bone Cancer . The vast majority of Bone Cancer queries asked for General Information -LRB- N = 1107 , 78 % -RRB- . An example of this category would be : `` Where is information on bone cancer ? '' Users asked questions about Bone Cancers linked to various sites of Anatomy as well as certain Histologies . There were some questions related to Bone Cancers in teenagers that were assigned to this category , rather than the Pediatric category . There were more questions about Diagnosis and Testing -LRB- N = 64 , 4.48 % -RRB- and Symptoms -LRB- N = 135 , 9.45 % -RRB- than Treatment -LRB- N = 26 , 1.82 % -RRB- . 2.0 Brain and Neurological Cancer Of the 1852 Brain and Neurological Cancers queries , General Information accounted for the vast majority -LRB- N = 1323 , 71.44 % -RRB- . There were 427 -LRB- 23.1 % -RRB- questions about specific cancer types in this category . Some cancer types queries asked about Medulloblastoma , which is typically but not always a Pediatric tumor . As with Bone Cancer above , some questions could have been meaningfully assigned to more than 1 top-level Cancer site category . In this category there were more queries about Symptoms -LRB- N = 259 , 13.98 % -RRB- than Treatment -LRB- N = 112 , 6.05 % -RRB- . 3.0 Breast Cancer As shown in Appendix 1 , Breast Cancer was one of the simpler cancer types , from a data-display standpoint . There was only 1 anatomic-cancer type and all of the individual queries for that cancer type were assigned into 1 of 10 subcategories . The 10 top-level Breast Cancer subcategories were : General Information -LRB- N = 3423 , 49.23 % -RRB- Symptoms -LRB- N = 889 , 12.79 % -RRB- Treatment -LRB- N = 570 , 8.20 % -RRB- Media/Organization -LRB- N = 428 , 6.16 % -RRB- Cause/Risk/Link -LRB- N = 393 , 5.65 % -RRB- Diagnosis and Testing -LRB- N = 376 , 5.41 % -RRB- Statistics -LRB- N = 274 , 3.94 % -RRB- Pictures -LRB- N = 225 , 3.24 % -RRB- Type -LRB- N = 217 , 3.12 % -RRB- Definition -LRB- N = 158 , 2.27 % -RRB- Nine of the 10 Breast Cancer subcategories were analyzed in detail in Appendix 1 . The tenth , Pictures , did not require further analysis . Most queries asked for General Information . There were more frequent queries about Breast Cancer -LRB- N = 6953 -RRB- than any other cancer type . This may not be apparent from Table 3 , which appears to show more D/G/B cancers -LRB- N = 8959 -RRB- . However , D/G/B overall is actually composed of 10 cancer types . The most frequently queried cancer type in D/G/B was Colorectal -LRB- N = 4,801 -RRB- which had fewer queries than Breast . Even though other cancer types may have been assigned more subcategories than the 10 for Breast , the detail and the medical specificity and technical vocabulary of Breast queries appear to be the most complex than other Cancer sites , probably reflecting the sophistication of basic research and clinical data on this topic and the relative sophistication of the breast cancer information seekers . 4.0 Cause and Risk There were 1249 queries in this highest-level category . Without mentioning a specific cancer by name , there were N = 1115 -LRB- 89.27 % -RRB- queries about Causes and Links but only N = 134 -LRB- 10.73 % -RRB- about Prevention . Among the 1115 queries in the Causes and Links subcategory , the following topics were noted : Drugs -LRB- N = 287 , 25.74 % -RRB- Unspecified -LRB- N = 247 , 22.15 % -RRB- -LRB- eg , `` What is cause a cancer ? '' -LSB- sic -RSB- -RRB- Radiation -LRB- N = 247 , 22.15 % -RRB- Personal -LRB- N = 116 , 10.40 -RRB- -LRB- eg , `` Can anti-persperant -LSB- sic -RSB- deodorant cause cancer ? '' -RRB- Chemical/Plastics -LRB- N = 74 , 6.64 % -RRB- Environmental -LRB- N = 70 , 6.28 % -RRB- Food Supplement -LRB- N = 64 , 5.74 % -RRB- Genetic Mutation/Virus -LRB- N = 10 , 0.90 % -RRB- Smoking was not in this list , probably because most queries about smoking were included under a query about a specific type of cancer , like Lung or Head and Neck . 5.0 Coping There were only 254 queries about Coping . The queries referenced Support Groups -LRB- N = 127 , 50 % -RRB- , Pain -LRB- N = 98 , 38.58 % -RRB- , and Depression -LRB- N = 29 , 11.42 % -RRB- . Even though there were few questions in this highest-level category , the issue was of specific interest to NCI , which asked for this category to be created and analyzed separately . 6.0 Diagnosis and Testing There were 3315 queries in this highest-level category , which did not mention a specific cancer by name . Most were queries about specific Testing -LRB- N = 2842 , 85.73 % -RRB- . The others -LRB- N = 473 , 14.27 % -RRB- were queries about Symptoms . Among Testing queries , CAT/CT scan -LRB- Computerized Axial Tomography/Computed Tomography scan -RRB- -LRB- N = 1509 , 53.10 % -RRB- and MRI -LRB- N = 587 , 20.65 % -RRB- were the most-common Testing topics , followed by Biopsy -LRB- N = 502 , 17.66 % -RRB- . 7.0 Digestive/Gastrointestinal/Bowel -LRB- D/G/B -RRB- The presentation of data queries for D/G/B in Appendix 1 is complex because , there were 7 top-level subcategories , including General Information and 10 cancer types identified in the General Information subcategory As shown in Appendix 1 , 8959 queries for D/G/B sites were broken down into 7 subcategories : General Information -LRB- N = 5568 , 62.15 % -RRB- Symptoms -LRB- N = 1506 , 16.81 % -RRB- Diagnosis and Testing -LRB- N = 1125 , 12.56 % -RRB- Treatment -LRB- N = 294 , 3.28 % -RRB- Statistics -LRB- N = 184 , 2.05 % -RRB- Definition -LRB- N = 163 , 1.82 % -RRB- Cause/Risk/Link -LRB- N = 119 , 1.33 % -RRB- Most queries asked for General Information . Examples of General Information queries would be `` Where can I learn about the cancer esophageal cancer ? '' ? and `` Where can I find information on Stomach cancer '' ? A breakdown of all D/G/B queries by cancer type is shown in the list below . The absolute numbers and percentages -LRB- of all D/G/B queries -RRB- in the list below differ from the pie diagram in Appendix 1 because the list below includes organ-type queries from General Information plus the 6 other subcategories in D/G/B . Colorectal -LRB- N = 4801 , 53.59 -RRB- Liver -LRB- N = 1413 , 15.77 % -RRB- Gastrointestinal -LRB- stomach -RRB- -LRB- N = 1094 , 12.21 % -RRB- Pancreas -LRB- N = 965 , 10.77 % -RRB- Bowel -LRB- N = 273 , 3.05 % -RRB- Esophagus -LRB- N = 260 , 2.90 % -RRB- Other -LRB- N = 153 , 1.7 % -RRB- The organ subsites in Other include Gall Bladder , Bile Duct , Anal , and Abdominal . As noted in Appendix 1 , for D/G/B there were far more questions about Symptoms -LRB- N = 1506 , 16.81 % -RRB- than Treatment -LRB- N = 294 , 3.28 % -RRB- possibly reflecting the fact that -LRB- 1 -RRB- users of Ask.com were just beginning their D/G/B information seeking and -LRB- 2 -RRB- there is less complexity in the published Treatment data for D/G/B compared to some other cancer types , like Breast Cancer . The terms Bowel , Gastrointestinal , Stomach , and Abdominal may have been used interchangeably by users . They appear not to recognize that queries for sigmoid , rectum , cecum , appendix , transverse colon , small bowel , and stomach -LRB- gastric -RRB- cancer would provide much more useful information . For D/G/B , some queries about Liver Metastases were included with queries about primary Liver Cancers . 8.0 General Research There were 7808 queries assigned to the highest-level category General Research , a topic not linked to a specific cancer type . In this category the 5 most-common subcategories were : Research -LRB- N = 2819 , 36.10 % -RRB- Organization -LRB- N = 1656 , 21.21 % -RRB- Clinical Trials -LRB- N = 1272 , 16.29 % -RRB- Concerns -LRB- N = 1201 , 15.38 % -RRB- Pictures -LRB- N = 559 , 7.16 % -RRB- Among the queries about Organization , there were 1065 queries about the American Cancer Society -LRB- ACS -RRB- and 223 about the National Cancer Institute -LRB- NCI -RRB- . Among the 1272 queries about Clinical Trials , the most-common 3 questions/topics were : What are ... -LRB- N = 634 , 49.84 % -RRB- eg , `` What are clinical trials ? '' Latest ... -LRB- N = 260 , 20.44 % -RRB- eg , `` latest cancer clinical trial research '' Types of ... -LRB- N = 111 , 8.73 % -RRB- eg , `` types of cancer trials '' 9.0 Genitourinary Cancers In decreasing order , the frequency of Genitourinary organ-type queries -LRB- N = 6250 -RRB- in all 12 Genitourinary subcategories including General Information was : Prostate -LRB- N = 3141 , 50.26 % -RRB- Testicular -LRB- N = 1772 , 28.35 % -RRB- Bladder -LRB- N = 708 , 11.33 % -RRB- Kidney -LRB- N = 496 , 7.94 % -RRB- Other -LRB- N = 133 , 2.12 % -RRB- Although it has been estimated that there were 198100 new cases of Prostate Cancer diagnosed in the US in 2001 and only 7200 cases of Testis Cancer -LSB- 34 -RSB- , the relative frequency of Testis Cancer queries was quite high . One possible reason might be that males diagnosed with Testis Cancer are generally much younger than those diagnosed with Prostate Cancer , and those younger individuals might be more-frequent information seekers on the Internet . It may also reflect the fact that the 2001 Tour de France bicycle race won by Lance Armstrong , a Testis Cancer survivor , was held during July , coinciding with the study period for this project . As with most sites , the most-common Prostate Cancer questions were General Information -LRB- N = 1715 , 54.6 % -RRB- . For Prostate Cancer , there were more questions about Treatment -LRB- N = 460 , 14.65 % -RRB- than Symptoms -LRB- N = 364 , 11.59 % -RRB- . This may reflect major medical controversies about treatment options and the typically asymptomatic presentation of the disease . For the Genitourinary category as a whole , there were more questions about Symptoms -LRB- N = 854 , 13.66 % -RRB- than Treatment -LRB- N = 604 , 9.66 % -RRB- . Expected misspellings of prostate -LRB- prostrate -RRB- were noted . 10.0 Gynecological Cancers There were 5344 queries overall . The breakdown of subcategories in decreasing frequency was : General Information -LRB- N = 3409 , 63.79 % -RRB- Symptoms -LRB- N = 939 , 17.57 % -RRB- Diagnosis and Testing -LRB- N = 452 , 8.46 % -RRB- Treatment -LRB- N = 247 , 4.62 % -RRB- Definition -LRB- N = 158 , 2.96 % -RRB- Cause/Risk -LRB- N = 83 , 1.55 % -RRB- Statistics -LRB- N = 42 , 0.79 % -RRB- Prevention -LRB- N = 14 , 0.26 % -RRB- In decreasing order of frequency , the cancer types queried in all 8 Gynecological subcategories included the following : Ovarian -LRB- N = 2031 , 38.00 % -RRB- Cervical -LRB- N = 1924 , 36.00 % -RRB- Uterine -LRB- N = 606 , 11.34 % -RRB- Endometrial -LRB- N = 225 , 4.21 % -RRB- Vulvar -LRB- N = 166 , 3.11 % -RRB- Vaginal -LRB- N = 219 , 4.09 % -RRB- Other or not specified -LRB- N = 173 , 3.24 % -RRB- There were nearly as many questions about Cervical Cancer as Ovarian Cancer despite the fact that in the United States in 2001 the estimated incidence of new Ovarian Cancers was about twice that of invasive Cervical Cancer -LSB- 34 -RSB- . There were questions about Endometrial cancer as well as Uterine cancer . These data suggest that Web site information needs to be provided using both labels . 11.0 Head and Neck There were 2522 queries overall . Most queries asked for General Information -LRB- N = 1485 , 58.88 % -RRB- . The vocabulary used to ask about specific cancer types within General Information was : Throat Mouth Oral Tongue Head Neck The vocabulary confirms the need to offer health information with words that are not technical like larynx , glottis , pharynx , or nasopharynx . There were 59 questions asking about Definitions of Head and Neck cancer . Specifics about cancer anatomy of this cancer type may be less familiar to the general public than other sites . There were 422 queries asking for Pictures of Head and Neck Cancer . There were only 47 questions -LRB- 1.86 % -RRB- asking about Cause/Risk/Link issues , despite the fact that there is a great deal known about the Causes and Prevention of Head and Neck Cancer . There were 418 questions -LRB- 16.57 % -RRB- about Symptoms and but only 52 -LRB- 2.06 % -RRB- about Treatment . 12.0 Hematologic and Blood Cancers Among 5448 queries in this category , the 5 most common of the 12 subcategories were : General Information -LRB- N = 3781 , 69.40 % -RRB- , Definition -LRB- N = 701 , 12.96 % -RRB- , Symptoms -LRB- N = 539 , 9.89 % -RRB- , Treatments -LRB- N = 175 , 3.21 % -RRB- , and Organizations -LRB- N = 102 , 187 % -RRB- . Within General Information users asked about Leukemia -LRB- N = 2895 , 76.57 % -RRB- , Myeloma -LRB- N = 592 , 15.66 % -RRB- , Bone Marrow -LRB- N = 148 , 3.91 % -RRB- , and Blood Cancers -LRB- N = 146 , 3.86 % -RRB- . Various misspellings of Leukemia were noted and nontechnical terms such as Blood Cancer and Bone Marrow Cancer were frequent . 13.0 Lung Cancer Lung Cancer -LRB- N = 4630 -RRB- accounted for 8 % of organ-type specific queries within the highest-level Cancer category . This is a disproportionately-low percentage given the relative incidence of Lung Cancer in the United States in 2001 -LSB- 32 -RSB- . There were more queries about Gynecological and Hematologic/Blood cancers , even though the US incidence for these is far lower . Among Lung Cancer queries , the queries were classified as follows : General Information -LRB- N = 3223 , 69.61 % -RRB- Symptoms -LRB- N = 530 , 11.45 % -RRB- Cause/Risk/Link -LRB- N = 305 , 6.59 % -RRB- Treatment -LRB- N = 219 , 4.73 % -RRB- Definition -LRB- N = 150 , 3.24 % -RRB- Statistics -LRB- N = 113 , 2.44 % -RRB- Diagnosis and Testing -LRB- N = 90 , 1.94 % -RRB- In the Cause/Risk/Link category of Lung Cancer , there were only N = 180 queries -LRB- 59.02 % -RRB- that asked generally about Causes of Lung Cancer and N = 102 queries -LRB- 33.44 % -RRB- that asked specifically about Smoking . There were N = 23 queries -LRB- 7.54 % -RRB- asking if Marijuana caused Lung Cancer . Only N = 255 -LRB- 7.91 % -RRB- queries within General Information asked about Lung Cancer by -LRB- histologic cell -RRB- Type , despite the fact that this is a major determinant of triage for treatment . For Lung Cancer > Treatment , there were 219 queries -LRB- 4.73 % -RRB- . Most Treatment queries were Unspecified -LRB- N = 118 , 53.88 % -RRB- , eg , `` What are treatments for lung cancer ? '' There were 26 Treatment questions about Cure -LRB- 11.87 % -RRB- . There were few specific questions about Medications -LRB- chemotherapy -RRB- -LRB- N = 21 , 9.59 % -RRB- , Radiation -LRB- N = 19 , 8.68 % -RRB- , or Surgery -LRB- N = 10 , 4.57 % -RRB- . Although all numbers were small , there were more questions about Alternative Treatment -LRB- N = 13 , 5.94 % -RRB- than Surgery -LRB- N = 10 , 4.57 % -RRB- . There were only 4 Treatment questions -LRB- 1.83 % -RRB- about palliative care , despite the grave prognosis for most Lung Cancers . Clearly the questions about Lung Cancer , the most-common lethal cancer , were far less sophisticated than the questions about either Breast Cancer or Prostate Cancer . 14.0 Lymphomas Among the 3333 queries about Lymphoma -LRB- including both Hodgkin 's Disease and Non-Hodgkin 's Lymphoma -RRB- , General Information -LRB- N = 2391 , 71.74 % -RRB- questions were the most common . Unlike many cancer types , there was frequent mention of histologic types , as is appropriate , given the wide variety of clinically-different prognoses and treatments in this subcategory . There were many different spellings of Hodgkin 's Disease . 15.0 Miscellaneous Cancers There were 1633 queries assigned to this Cancer subcategory . The Miscellaneous Cancers were : Endocrine -LRB- N = 901 , 55.17 % -RRB- Neoplasm -LRB- N = 272 , 16.66 % -RRB- Kaposis -LRB- N = 262 , 16.04 % -RRB- Ocular -LRB- N = 179 , 10.96 % -RRB- Germ Cell -LRB- N = 19 , 1.16 % -RRB- Several of the Ocular queries , eg , Ocular Melanoma and Retinoblastoma , could have been considered for other subcategories , such as Skin and Pediatric respectively . Germ cell tumors could also have been placed in either Genitourinary or Gynecological subcategories . These ambiguities illustrate the difficulty in categorizing precise user information needs despite the use of natural language processing . 16.0 Pediatric There were only 603 Pediatric queries , and most asked about a specific cancer type -LRB- N = 403 , 66.83 % -RRB- . There were relatively few General Information queries -LRB- N = 81 , 13.43 % -RRB- eg , `` where can I find information on children 's cancers ? '' Since patients with Pediatric cancers in the US are usually managed generally by pediatric oncology specialists at major regional medical centers , those seeking Pediatric cancer information are probably directed to specialized Web sites rather than general sites like Ask.com . Of 403 queries for cancer types , the most common were Hematologic/Blood -LRB- N = 137 , 34 % -RRB- , Neuroblastoma -LRB- N = 133 , 33 % -RRB- , and Rhabdomyosarcoma -LRB- N = 68 , 16.87 % -RRB- . There were only 4 questions referring to pediatric Brain and Neurological cancers . Since this is such a common Pediatric tumor type , it is possible that some Pediatric neurological tumor questions were assigned to the Brain and Neurologic Cancer category even though the questions were really meant to target a Pediatric issue . 17.0 Skin Cancers Among 6709 queries in this Cancer subcategory , 3596 -LRB- 53.60 % -RRB- asked for General Information . Like Lymphoma , there was frequent mention of specific Skin Cancer types -LRB- N = 2157 , 32.15 % -RRB- , probably because of the significantly-different clinical prognoses and treatments . Only 169 queries -LRB- 2.52 % -RRB- asked about Cause/Risk/Link , and 60 queries -LRB- 0.89 % -RRB- asked about Prevention despite the fact that so much is known about these topics and Skin Cancer . Among Skin Cancers queried by histologic cancer type -LRB- N = 2157 , 32.15 % -RRB- , Melanoma was the most common -LRB- N = 1707 , 79.14 % -RRB- , even though it is far-less common than Basal Cell Skin Cancers -LRB- N = 322 , 14.93 % -RRB- -LSB- 10 -RSB- . Frequent mention of Melanoma probably reflects its more-serious prognosis and more-complicated clinical triage . 18.0 Soft Tissue Cancers There were 3954 queries in this Cancer subcategory . Although most appropriately refer to sarcomas of various types , there was a minority of misplaced queries . Some queries appear to reference conditions that are probably benign -LRB- Ganglion , Fibroid , Dysplasia , and Lipoma -RRB- and others should have been placed in different Cancer subcategories eg , Brain and Neurological -LRB- Oligodendroglioma and Glioma -RRB- These will be corrected on later analyses . 19.0 Treatment In the 3832 highest-level category queries about Treatment , most questions were about a specific Treatment Type -LRB- N = 3223 . 84.11 % -RRB- , even though no specific cancer was mentioned . Within Treatment > Treatment Type there were many general queries about Chemotherapy -LRB- N = 2275 , 70.59 % -RRB- . There were questions about general Radiation Therapy -LRB- N = 534 , 16.57 % -RRB- , and few about specialized Radiation Therapy treatments like Gamma Knife , Laser , and Protons . There were more general questions about Alternative Therapies -LRB- N = 239 , 7.42 % -RRB- than Surgery -LRB- N = 127 , 3.94 % -RRB- Many Alternative Therapy questions also appear in specific organ-type subcategories , particularly Breast . Query Frequency Relative to US Incidence of Cancer Types Table 4 compares the incidence of selected cancers in the United States -LRB- US -RRB- in the year 2001 with the frequency of selected site-specific cancer queries in this report . It has been estimated that there were 1268000 new cancer cases in the US in 2001 -LSB- 34 -RSB- . The sites in Table 4 were selected specifically because they were easiest to compare directly . The relative percentage of specific organ-type queries exceeds the percentage of annual incidence only for rarer cancers . The difficulty of finding useful information on prominent cancer portals or with standard search engines may be one explanation , although there are others . The comparison is not meant to be definitive as there are clearly issues with validity of this comparison : Cancer prevalencemight be a better benchmark than incidence US incidence data exclude cases of in situ breast and cervix cancers as well as the very-common basal cell and squamous cell skin cancers Queries could have come from anywhere in the world , not just the United States Query total may include those who accessed the site more than once Queries could have come from individuals who are not newly-diagnosed patients Table 4 Comparing relative annual US incidence of selected cancers and query frequency Cancer Site Estimated Number of New US Cancers Diagnosed in 2001 * % of Estimated New US Cancers in 2001 * † Number of Cancer Site-Specific Queries in This Report % Queries in Cancer Category † ‡ Digestive -LRB- D/G/B -RRB- 235700 18.6 8959 15.0 Prostate 198000 15.6 3141 5.3 Breast 193700 15.3 6953 11.7 Lung 169000 13.3 4630 7.8 Lymphoma 63600 5.0 3333 5.6 Bladder 54300 4.3 708 1.2 Uterus/Endometrial 38300 3.0 931 1.6 Head and Neck 30100 2.3 2522 4.2 Ovary 23400 1.9 2031 3.4 Brain and Neurological 17200 1.4 1852 3.1 Cervix 12900 1.0 1924 3.2 Soft Tissue 8 700 0.69 3954 6.6 Testis 7200 0.57 1772 3.0 * Data from 2001 Estimated Annual US Cancer Incidence Figures -LRB- N = 1268000 -RRB- -LSB- 10 -RSB- . † Percentages in columns 3 and 5 do not add up to 100 % because only selected cancers were included in this chart . ‡ Only selected Cancers were included in this chart . Denominator -LRB- N = 59619 -RRB- was the total number of queries about specific subsites in the Cancer category . Other Observations The query analysis reveals that online users generally seek information about Symptoms and Treatment for specific cancers , rather than about cancers generally . In addition , Symptom queries showed a frequency between 2 and 5 times that of Treatment queries , for most cancers . For this study we did not specifically target queries about Adult Immune Deficiency Syndrome -LRB- AIDS -RRB- , even though AIDS can often be associated with Cancer . There were 262 questions about Kaposi 's Sarcoma in the Miscellaneous Cancers category . Discussion General Information was the largest category for almost all cancers , probably reflecting the nature of the Ask.com consumer search engine . It is a consumer-oriented Web-wide search engine where users tend to seek general information that can help them learn either how or where they should further pursue their inquiries . It is likely the users are just starting their Web searches on Ask.com and they are not yet interested in or they do not yet know enough information to ask more-sophisticated questions . This behavior may not reflect that of users who go directly to a known cancer-information portal with a predetermined need for detailed information . We attempted to capture and analyze all cancer-related queries , including those with correct and incorrect spellings . Misspellings were noted relatively frequently , but we have no data on the number of misspellings , as we did not target this in advance as an endpoint , and we did not have direct access to the raw data logs . Appendix 1 shows verbatim queries with examples of the misspellings . Automating help for users who enter misspelled words is a major issue for search engines in order to optimize query results . Other researchers have noted the search difficulties related to spelling of cancer search terms correctly -LSB- 35 -RSB- . Ask.com users entered both keyword searches and sentence-style queries , despite the fact that this is a natural-language-processing search engine . We recognize that even if users typed in a long query it was still sometimes difficult to discern absolutely what specific information the user needed , particularly since we did not have access to the links users picked . The vocabulary employed by users of Ask.com ranged from unsophisticated to very sophisticated . This suggests that allowing users to employ less-technical language on cancer Web sites would significantly help them find the information they seek . The queries captured for this study undoubtedly reflect the news and research studies in the public arena during the time period from June to August 2001 . A different time period would certainly reflect a different distribution . Examples of the kinds of events that could affect the results include the diagnosis or death of a celebrity with cancer , the publication of a major trial about bone marrow transplantation for breast cancer , or the Food and Drug Administration approval of an important new drug . The presence of a search engine with natural language processing on a Web site , while potentially valuable to users , does not obviate the need for good user-centered Web site design and information architecture -LSB- 36 -RSB- . It has been shown that searching via search engine can be minimized and user satisfaction maximized if information architecture and link titles follow appropriate guidelines -LSB- 37 -RSB- . Nevertheless , for less-sophisticated users , a natural-language-processing search engine can be helpful in finding the information users seek and provide enhanced success in searching . An October 30 , 2003 search of the PubMed Web site -LSB- 38 -RSB- of the National Library of Medicine -LSB- 39 -RSB- yielded 458 search results from a query for `` Natural Language Processing . '' Most citations were from publications within the last 3 years , attesting to the currency of natural language processing as an important research topic cutting across a wide variety of research disciplines . Potential data-mining applications of this tool in medicine extend far beyond the use described in this paper . Eysenbach and Kohler have recently developed a novel methodology , similar to the method used in this study , to estimate the actual volume and prevalence of health-related searches on the Web in relation to the total number of searches conducted daily on the Internet -LSB- 40 -RSB- . They collected queries from 2 search engines , Metacrawler -LRB- a search engine of search engines -RRB- -LSB- 41 -RSB- and Ask.com -LSB- 29 -RSB- -LRB- the same natural-language-processing search engine used for this report -RRB- . These 2 search engines were selected because they allowed `` peeking '' at actual user search-query topics . They concluded that 4.5 % of all searches on the Web might be health related . The queries were collected from Metacrawler between February 2001 and April 2002 , and from Ask.com between February 2001 and April 2001 . The first date range overlapped our study dates and the second occurred just before data collection for our study . In summary , natural-language-processing tools such as the one used for this study are able to filter and subset raw query data into useful analysis categories . Retrieval and analysis of these data can be used to better understand the actual content users want and the level of understanding and sophistication they have when they come to the Web site . Using the information on a continuing basis can form the basis for updating content on Web sites based on the most-current user needs . If a natural-language search engine were offered on a health-information portal , for example , it could improve customer access to desired information , particularly for those users with less sophistication about content or language . Additional analyses of query results are planned for the future . Consideration has been given to piloting the use of natural language processing on subsites of our Web portal .

O O B O O O B O O O O O O O O O O O O O O O O O O O B O O O O O O O O B I O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O B O O O O O O O O O O B O O O O B O O O O O O O O O O O O O O B O O B O O O O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O B O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O B O O B O O O O O O O O O O O O O O O B O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O B O O O O O O B O O O O O O O O O O O B O O O O O O O O O O O O B O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O B O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O B O O O O O O O O B O O O O O O O O O B O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O B O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B I I O O O B O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O B O O O O O B O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O B O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O B O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O B O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O B O O O B O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O B O O O O O B O B O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O B O O O O O O O O O O O B O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O B I O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O

cancer
internet
search engines
natural language processing


Celastrol inhibits polyglutamine aggregation and toxicity though induction of the heat shock response Heat shock proteins -LRB- hsps -RRB- are protective against the harmful effects of mutant expanded polyglutamine repeat proteins that occur in diseases such as Huntington 's , prompting the search for pharmacologic compounds that increase hsp expression in cells as potential treatments for this and related diseases . In this paper , we show that celastrol , a compound recently shown to up-regulate hsp gene expression , significantly decreases killing of cells expressing mutant polyglutamine protein . This effect requires the presence of the transcription factor responsible for mediating inducible hsp gene expression , HSF1 , and is correlated with decreased amounts and increased sodium dodecyl sulfate -LRB- SDS -RRB- solubility of polyglutamine aggregates . These results suggest the potential of celastrol as a therapeutic agent in the treatment of human polyglutamine expansion diseases . Introduction A large number of neurodegenerative diseases , including Huntington 's and Kennedy 's diseases , are characterized by expanded polyglutamine repeats in certain proteins leading to their aggregation and toxic effects on the cells expressing them -LSB- 1 , 2 -RSB- . A number of results suggest that polyglutamine-containing aggregates are important both for the initiation and progression of these diseases -LSB- 3 , 4 -RSB- . Polyglutamine protein aggregates in brains of patients and transgenic animals have been found to contain various molecular chaperones , ubiquitin , and components of the 20S proteasome -LSB- 5 , 6 -RSB- . This suggests that neuronal cells recognize the protein aggregates as abnormally folded and try to disaggregate or degrade the mutant proteins by recruiting chaperones and proteasomal components -LSB- 7 -RSB- . Consistent with this view , increased expression of hsp70 and other heat shock proteins -LRB- hsps -RRB- has been found to be effective in reducing the toxicity of mutant polyglutamine proteins , suggesting the potential of pharmacological up-regulation of hsp gene regulation as a means for treating polyglutamine expansion diseases such as Huntington 's -LSB- 8 -- 11 -RSB- . Heat Shock Factor 1 , HSF1 , is the transcription factor responsible for up-regulating the expression of hsp70 and other hsp genes in response to cellular stress -LSB- 12 , 13 -RSB- . HSF activation involves stress-induced conversion of this factor to its trimeric DNA-binding form -LSB- 14 -- 17 -RSB- . The activated trimeric HSF1 then binds to heat shock elements in the promoters of hsp genes to up-regulate their transcription , ultimately leading to elevated levels of cytoprotective hsps in these cells . The function of HSF1 as a key positive regulator of hsp expression , coupled to the observed ability of hsp proteins to protect cells from polyglutamine toxicity prompted the hypothesis that interventions that lead to activation of HSF1 could provide protection for cells against this toxicity and possibly represent agents that could be useful in treating polyglutamine expansion diseases such as Huntington 's -LSB- 10 , 18 -RSB- . Consistent with this hypothesis , expression in cells of a constitutively active mutant HSF1 protein results in decreased polyglutamine toxicity and aggregate formation in both cell culture and animal models -LSB- 19 , 20 -RSB- . These and other results have stimulated the search for pharmacological compounds capable of up-regulating hsp gene expression , as such compounds would theoretically have potential as treatments for these and other diseases of protein misfolding . Recently , it was demonstrated that a compound called celastrol , which is found in the Celastraceae family of plants and is already being used to treat diseases such as rheumatoid arthritis , bacterial infection , and fever -LSB- 21 , 22 -RSB- , is able to activate HSF1 and up-regulate hsp gene expression -LSB- 18 -RSB- . Celastrol has previously been found to inhibit the aggregation of purified Q58-huntingtin 1-171 amino terminal fragment in an in vitro assay , although this study did not examine effects of celastrol on polyglutamine aggregation in cells -LSB- 23 -RSB- . Celastrol has also been found to inhibit the cytotoxicity of expanded polyglutamine form of huntingtin exon 1 -LSB- 24 -RSB- and androgen receptor -LSB- 25 -RSB- , but these studies did not examine the mechanism by which celastrol protects cells from expanded polyglutamine protein cytotoxicity . The results of the experiments presented in this paper demonstrate that celastrol is effective in preventing both the aggregation and toxicity of polyglutamine expression in cells and that it mediates these effects via the HSF1-mediated gene expression pathway . These results support the potential of this drug as a possible therapeutic agent for treating polyglutamine expansion diseases . The results also suggest that other drugs that stimulate HSF1 activity leading to hsp gene expression may also have beneficial activity against these disease states as well as other human diseases that are caused by protein misfolding . Materials and methods Plasmids , cell culture , and celastrol The expression constructs encoding Q19-YFP , Q57-YFP , and Q81-YFP were kindly provided by Dr. James Burke -LRB- Duke University -RRB- . The polyglutamine-YFP -LRB- Qn-YFP -RRB- vectors were constructed using CAG repeats that were synthesized by polymerase chain reaction -LRB- PCR -RRB- from human dentatorubral pallidoluysian atrophy -LRB- DRPLA -RRB- cDNAs containing different CAG repeats . HeLa cells were cultured in Dulbecco 's modified Eagle 's medium -LRB- DMEM ; Cellgro -RRB- supplemented with 10 % fetal bovine serum -LRB- FBS -RRB- and 50 μg/ml gentamicin . PC12 cells were cultured in DMEM medium -LRB- Cellgro -RRB- supplemented with 5 % FBS and 10 % heat-inactivated horse serum -LRB- Gibco -RRB- , and 100 units/ml Penicillin-Streptomycin -LRB- Gibco -RRB- . HSF1 + / + and HSF1 − / − mouse embryo fibroblast -LRB- MEF -RRB- cells -LRB- kindly provided by Dr. Ivor Benjamin , University of Utah -RRB- were cultured in DMEM medium -LRB- Cellgro -RRB- supplemented with 10 % FBS , 1 × Penicillin-streptomycin -LRB- Gibco -RRB- , and 1 × Non-essential amino acids -LRB- Gibco -RRB- . Transfection was performed using Effectene transfection reagent -LRB- Qiagen -RRB- , following the manufacturer 's protocol . Celastrol -LRB- Calbiochem -RRB- was dissolved in dimethyl sulphoxide at a stock concentration of 5.54 mM . Trypan blue cell viability assay Cells were collected by centrifugation at 1,000 rpm for 10 min at 4 °C , and the pellet was washed twice with 1 × phosphate-buffered saline -LRB- PBS -RRB- . The cell pellet was then resuspended in 1 × PBS to a concentration of approximately 106 cells/ml . A 1:1 dilution of the suspension was prepared using a solution containing 0.4 % trypan blue stain -LRB- Gibco -RRB- , and the suspension was then loaded into the counting chamber of a hemocytometer . The number of stained cells as well as the total number of cells was counted , and the percentage of stained cells was taken to represent the percentage of cell death . Experiments were repeated three times . Fluorescence microscopy Cells were seeded on coverslips , and 48 h after transfection , Hoechst 33342 and verapamil were added to the medium to final concentrations of 5 and 50 μg/ml , respectively . After incubation at 37 °C for 30 min , the medium was removed , and the coverslips washed with 1 × PBS for 5 min . A solution containing 3.7 % paraformaldehyde in 1 × PBS was added , and after 20 min incubation at room temperature , coverslips were washed with 1 × PBS for 5 min . Coverslips were washed briefly three times in distilled water and mounted onto a slide spotted with 15 μl Vectashield -LRB- Vector Laboratories -RRB- . Excess fluid was wicked from the coverslip and the edges of the coverslip sealed with fingernail polish . The fluorescence was then visualized using a Nikon fluorescent microscope and pictures taken with a Nikon Spotcam digital-imaging camera . To quantify the formation of polyglutamine aggregates , visual fields which contained similar numbers of cells -LRB- based on the density of nuclei stained by Hoechst -RRB- were chosen under 20 × objective , and then the number of cells that contained aggregates in each field of vision was counted . Three different visual fields were quantified in each case . There were approximately 300 cells in each visual field for the experiments involving HeLa cells and PC12 cells , and approximately 100 cells in the experiments involving MEF cells . We scored a cell as positive if it had any visible aggregates , and the aggregates in the majority of cells appeared to be intranuclear or perinuclear -LRB- example shown in Fig . S1 -RRB- . Extract preparation and Western blot assay Cell lysis was performed on ice for 30 min in 50 mM Tris -- HCl -LSB- pH 8.0 -RSB- , 100 mM NaCl , 5 mM MgCl2 , 0.5 % NP40 lysis buffer -LSB- 26 -RSB- containing 1 × protease inhibitor cocktail -LRB- Roche -RRB- , and 1 mM phenylmethanesulfonyl fluoride added . To examine hsp70 level , the cell lysate was cleared by centrifugation at 1,000 rpm at 4 °C for 10 min , and the protein concentration of the supernatant was then determined by BioRad assay . Five micrograms of protein extract was subjected to sodium dodecyl sulfate-polyacrylamide gel electrophoresis -LRB- SDS-PAGE -RRB- and Western blot following standard procedures . The antibodies and dilutions used to probe the Western blots were as follows . Anti-hsp70 -LRB- stress-induced form ; Stressgen -RRB- was used at 1:10,000 , anti-β-actin antibody -LRB- Sigma -RRB- was used at 1:10,000 , and the anti-green fluorescent protein -LRB- GFP -RRB- polyclonal antibody -LRB- Bethyl labs -RRB- was used at a dilution of 1:4,000 . Hsp70 Western blots were quantified using ImageQuant software . Filtration assay Cell lysis was performed on ice for 30 min in the lysis buffer described above . The insoluble fraction was obtained by centrifugation of the cell lysate at 14,000 rpm at 4 °C for 10 min . The pellet was resuspended in DNase buffer -LRB- 20 mM Tris -- HCl -LSB- pH 8.0 -RSB- , 15 mM MgCl2 , and 0.5 mg/ml DNase I -RRB- , followed by incubation at 37 °C for 1 h . After DNase treatment , the protein concentration was determined by analyzing an aliquot using the BioRad assay . The incubation was terminated by adjusting the mixtures to 20 mM ethylenediamine tetraacetic acid -LRB- EDTA -RRB- , 2 % sodium dodecyl sulfate -LRB- SDS -RRB- , and 50 mM dithiothreitol and boiling for 5 min . Thirty micrograms of protein was diluted into 200 μl 2 % SDS , and this was then filtered through a 0.2-μm pore size cellulose acetate membrane -LRB- Schleicher & Schuell -RRB- that was equilibrated with 2 % SDS using a Dot-Blot system -LRB- Schleicher & Schuell -RRB- . Filters were washed twice with 200 μl 0.1 % SDS and then subjected to anti-GFP Western blot -LSB- 27 -RSB- . SDS solubility assay To perform the SDS solubility assay , cell lysates were prepared as described above for the filtration assay , including the 14,000 rpm centrifugation of the cell lysate and DNase treatment and protein concentration determination by BioRad assay . Forty micrograms of protein was then adjusted to 2 × SDS-PAGE sample buffer -LRB- 62.5 mM Tris · HCl -LSB- pH 6.8 -RSB- , 10 % glycerol , 2 % SDS , 5 % β-mercaptoethanol -RRB- and boiled for 5 min after which the sample was analyzed by SDS-PAGE and Western blot with anti-GFP polyclonal antibody . Statistical analysis Statistical significance was determined using the Student 's t test . A P value of < 0.05 was considered to be statistically significant . Results Celastrol protects against polyglutamine toxicity The experimental system we chose to test for effects of celastrol on polyglutamine aggregation and toxicity is transfection of a Q57-YFP fusion protein into cell lines -LRB- gift of Dr. James Burke -RRB- , which is an established model for the aggregation of polyglutamine containing proteins in vivo -LSB- 28 -RSB- . Consistent with this , the Q57-YFP protein forms clearly visible aggregates upon expression in HeLa cells , while cells transfected with Q19-YFP exhibit a diffuse pattern -LRB- Fig . S1 -RRB- . A previous study showed that 8 h of celastrol treatment leads to increased levels of the hsp70 protein -LSB- 18 -RSB- . As the studies described in this paper include cell death and aggregation assays performed at 48 h after transfection with the Q57-YFP construct , we wanted to ensure that cells treated with celastrol treatment for longer times also exhibit elevated hsp70 expression . As shown in Fig. 1a -LRB- upper panel -RRB- , celastrol treatment of HeLa cells for 24 or 48 h both result in a dose-dependent increase in expression of the hsp70 protein . Quantification of these hsp70 Western blot results indicates that the induction of hsp70 by each celastrol concentration is very similar at 24 and 48 h , showing that treatment with this drug results in a sustained increase in hsp70 levels -LRB- lower panel -RRB- . Next , to test whether treatment with this drug confers protection from polyglutamine toxicity , HeLa cells were transfected with the Q57-YFP construct and then incubated in media containing different concentrations of celastrol . The results of this experiment , shown in Fig. 1b , reveal that celastrol treatment is associated with a significant decrease in death of these cells expressing this mutant polyglutamine protein . Fig. 1Celastrol treatment reduces Q57-YFP cytotoxicity . a Hsp70 protein level is increased by celastrol treatment . HeLa cells were treated with the indicated concentrations of celastrol for 24 or 48 h , after which cell extracts were made and subjected to Western blot using antibodies against hsp70 or β-actin -LRB- upper panel -RRB- . These results were quantified using the ImageQuant program , and the values for the two treatment times , grouped by celastrol concentration , were graphed -LRB- lower panel -RRB- . b HeLa cells were transfected with Q57-YFP along with celastrol treatment at the concentrations indicated . After 48 h , the amount of cell death was determined by trypan blue assay . Data are shown as means ± SE -LRB- * P < 0.007 , ** P < 0.004 , *** P < 0.0001 , for each celastrol concentration treatment vs no celastrol -RRB- Celastrol protective effect are correlated with decreased number of cells containing polyglutamine aggregates To determine whether the ability of celastrol to protect cells from polyQ toxicity could be mediated via effects on polyQ aggregates , we then examined whether celastrol treatment alters the number of polyQ aggregates in cells and/or their solubility . The results of these experiments show that treatment with celastrol is associated both with a decrease in the number of cells containing Q57-YFP aggregates , as quantified by fluorescence microscopy -LRB- Fig. 2a -RRB- , and also with an increase in the amount of Q57-YFP that can be solubilized from aggregates by SDS treatment -LRB- Fig. 2b -RRB- . SDS solubility of aggregates is related with their toxicity , and data from previous studies suggests that molecular chaperones may ameliorate the neurodegenerative effect of mutant polyglutamine protein , at least in part , by increasing the solubility of these proteins -LSB- 9 , 29 -RSB- . Together , the results shown in Figs. 1 and 2 indicate that celastrol treatment decreases the death of cells expressing mutant polyglutamine protein and also decreases the number of cells containing Q57-YFP-aggregates and the insolubility of Q57-YFP-aggregates . Fig. 2Celastrol treatment reduces number of cells containing Q57-YFP aggregates and increases Q57-YFP solubility . HeLa cells were transfected with Q57-YFP along with celastrol treatment at the concentrations indicated . a After 48 h of transfection , the formation of Q57-YFP aggregates was quantified using fluorescence microscopy . Visual fields which contained similar numbers of cells -LRB- based on the density of nuclei stained by Hoechst -RRB- were chosen under 20 × objective , and then the number of aggregates in each field of vision was counted . Three different visual fields were quantified in each case , and data are shown as means ± SE -LRB- * P < 0.004 , ** P < 0.001 , *** P < 0.0003 , for each celastrol concentration treatment vs no celastrol -RRB- . b To determine the amount of Q57-YFP monomer that could be solubilized from aggregates in lysates of the transfected cells by SDS treatment , the protein concentration of the insoluble fraction of the cell lysates was determined , and then 40 μg of protein was subjected to SDS solubilization treatment , followed by Western blot using anti-GFP antibody Protective effects of celastrol treatment in PC12 cells As the deleterious effects of expression of mutant polyglutamine proteins in vivo are observed primarily in cells of neuronal origin , we next sought to test the effect of celastrol treatment on polyglutamine toxicity and aggregation in PC12 cells . As shown in Fig. 3a , PC12 cells are very sensitive to killing by expression of the Q57-YFP protein , but as observed for HeLa cells in Fig. 1b above , celastrol treatment results in a significant decrease in death of these cells . As was observed in the experiments using HeLa cells , treatment of PC12 cells with celastrol resulted in a significant decrease in the number of polyglutamine aggregates in the cells -LRB- Fig. 3b -RRB- and an increase in the amount of Q57-YFP that can be solubilized from aggregates by SDS treatment -LRB- Fig. 3c -RRB- . Interestingly , the concentrations of celastrol required to achieve these effects on polyglutamine toxicity and number and solubility properties of aggregates in PC12 cells were significantly lower than that needed for HeLa cells . Fig. 3Celastrol effects on Q57-YFP toxicity and aggregates in PC12 cells . PC12 cells were transfected with Q57-YFP along with celastrol treatment at the concentrations indicated . After 48 h , the amount of cell death was determined by trypan blue assay -LRB- a -RRB- , the number of cells containing Q57-YFP aggregates was quantified using fluorescence microscopy -LRB- b -RRB- , and the amount of Q57-YFP monomer solubilized from aggregates by SDS treatment visualized by Western blot using anti-GFP antibody -LRB- c -RRB- . In a and b , data are shown as means ± SE -LSB- * P < 0.0001 and ** P < 0.0001 -LRB- a -RRB- , * P < 0.001 and ** P < 0.0001 -LRB- b -RRB- , in each case for each celastrol concentration treatment vs no celastrol -RRB- HSF1 − / − cells exhibit increased polyglutamine aggregation and toxicity Inducible hsp expression is thought to be important for the ability of cells to decrease polyglutamine toxicity and aggregation -LSB- 8 -- 11 -RSB- . Consistent with this hypothesis , HSF1 − / − MEFs -LSB- 30 -RSB- that are transfected with Q57-YFP exhibit a higher incidence of cell death than HSF1 + / + MEF cells -LRB- Fig. 4a -RRB- . The HSF1 − / − MEFs also show increased Q57-YFP aggregation , both as measured by numbers of aggregates using fluorescence microscopy of cells -LRB- Fig. 4b -RRB- and by amount of aggregated Q57-YFP protein present in cell extracts detected by filtration assay -LRB- Fig. 4c -RRB- . Fig. 4HSF1 − / − cells exhibit higher Q57-YFP aggregation and cell death . HSF1 − / − and wild-type MEF cells were transfected with Q57-YFP , and after 48 h , cell death was examined by trypan blue assay -LRB- a -RRB- , and the number of cells containing Q57-YFP aggregates was quantified using fluorescence microscopy -LRB- b -RRB- , or filtration assay , in which 30 μg of the insoluble fraction was filtered through 0.2 μm cellulose acetate membrane , and the aggregates retained on the membrane were immunoblotted using anti-GFP antibody -LRB- c -RRB- . In a and b , data are shown as means ± SE . * P < 0.008 -LRB- a -RRB- ; * P < 0.003 -LRB- b -RRB- Protective effects of celastrol require HSF1 Based on previous results showing the ability of celastrol to activate HSF1 leading to increased hsp gene expression -LSB- 18 -RSB- , we hypothesized that this drug 's ability to decrease polyglutamine toxicity and aggregation shown by the results of Figs. 1 , 2 , and 3 above could be mediated via the HSF1-regulated gene expression pathway . To test this hypothesis , we compared the effect of celastrol treatment on polyglutamine toxicity and aggregation in the HSF1 + / + vs HSF1 − / − MEF cells . Celastrol does not exhibit any toxicity in HeLa cells at 1.6 μM , but we found its optimal concentration in MEF cells to be lower than that of HeLa cells . Thus , for these experiments , we used 0.4 μM celastrol treatment , which has no toxic effect in either HSF1 − / − or wild-type MEFs -LRB- data not shown -RRB- . First , Western blot analysis shows that the HSF1 − / − cells are unable to up-regulate hsp70 protein expression in response to celastrol treatment , demonstrating that celastrol-induced up-regulation of hsp70 expression is mediated by the HSF1-regulated gene expression pathway -LRB- Fig. 5a -RRB- . Fig. 5Celastrol decreases Q57-YFP toxicity and aggregation in wild-type but not HSF1 − / − MEF cells . a Celastrol treatment does not induce hsp70 expression in HSF1 − / − cells . Wild-type and HSF1 − / − MEF cells were treated with no celastrol or 0.4 μM celastrol , and after 12 h , cell extracts were made , and 5 μg protein was loaded into each lane for immunoblotting with anti-hsp70 antibodies . b and c Wild-type and HSF1 − / − MEF cells were transfected with Q57-YFP , with celastrol at the indicated concentrations added at the same time . After 48 h , cell death was examined by trypan blue assay -LRB- b -RRB- , with data shown as means ± SE -LRB- * P < 0.002 for HSF2 + / + 0.4 μM celastrol vs no celastrol ; * P < 0.002 for HSF2 − / − 0.4 μM celastrol vs no celastrol -RRB- , and amount of aggregated Q57-YFP was determined by filtration assay followed by anti-GFP Western blot -LRB- c -RRB- The results shown above -LRB- Figs. 1 , 2 , 3 , and 5a -RRB- indicate that celastrol treatment is effective in preventing polyglutamine aggregation and toxicity and that this drug up-regulates hsp70 expression by an HSF1-dependent mechanism . These results suggest that celastrol treatment protects cells by stimulating HSF1-dependent expression of hsps . However , celastrol could also modulate other cellular activities that could contribute to these protective effects . Thus , to test the contribution of HSF1-regulated pathways in these protective effects , we transfected HSF1 + / + and HSF1 − / − MEF cells with Q57-YFP in combination with celastrol treatment and then measured the amount of cell death and levels of aggregated Q57-YFP . The results of this experiment show that , as it did for the HeLa and PC12 cells in the experiments of Figs. 1 and 3 above , celastrol treatment resulted in decreased death of HSF1 + / + MEF cells transfected with Q57-YFP , but treatment with this drug did not protect the HSF1 − / − cells -LRB- Fig. 5b -RRB- . Consistent with these results , the HSF1 + / + cells , but not the HSF1 − / − cells , showed decreased levels of aggregated Q57-YFP upon celastrol treatment , as measured by the filtration assay -LRB- Fig. 5c -RRB- . These results suggest that the HSF1 protein plays an important role in the protective effects of celastrol against polyglutamine toxicity and aggregation . Discussion The results described above indicate that the drug celastrol is able to decrease polyglutamine toxicity , supporting the proposal that this drug could potentially be useful in the treatment of Huntington 's disease and possibly other human polyglutamine expansion disorders . In support of this possibility , it has been found that celastrol treatment of mice results in elevation of hsp70 levels in neurons in the brains of these mice -LSB- 31 -RSB- . The results also show that lower concentrations of celastrol are required for its protective effects against polyglutamine toxicity in PC12 cells , cells with neuronal characteristics , compared to other cell types . A particularly beneficial aspect of this drug is that it is already being used to treat people with other disorders -LSB- 21 , 22 -RSB- , and thus it could likely be adapted more quickly for use in treating polyglutamine expansion diseases than other drugs not currently being used in humans . Results presented in this paper indicate that the protective effect of celastrol against polyglutamine toxicity is associated with decreased numbers of cells containing aggregates as well as increased SDS-solubility of the mutant polyglutamine protein . This might seem to be at odds with findings that formation of polyglutamine aggregates can be protective for cells expressing these mutant proteins -LSB- 32 -- 34 -RSB- . However , our results are consistent with the findings of a number of studies which indicate that molecular chaperones can reduce the formation of polyglutamine aggregates and increase the solubility of expanded polyglutamine proteins -LSB- 8 -- 11 , 19 , 20 -RSB- . While one study found that hsp40 overexpression , but not hsp70 overexpression , was associated with reduced aggregation -LSB- 35 -RSB- , the results of a number of studies do suggest that chaperones are able to reduce aggregation of mutant polyglutamine proteins . Molecular chaperones could be acting at multiple levels to reduce polyglutamine toxicity , but one possibility that has been proposed is that these chaperones , and by extension celastrol through its HSF1-mediated up-regulation of chaperones , may be acting at an early step to prevent formation of toxic intermediates before they can become part of larger aggregates -LSB- 8 -- 11 , 19 , 20 -RSB- . This would explain both how celastrol treatment protects cells from polyglutamine toxicity and the effects we observe on polyglutamine aggregates in the celastrol-treated cells . An alternative explanation that must be considered , in keeping with previous studies indicating that aggregate formation can be protective for cells -LSB- 32 -- 34 -RSB- , is that the results we observe relating celastrol with the numbers of cells containing polyglutamine aggregates could be due , at least in part , to a potential ability of celastrol to enhance the survival of cells that do not contain aggregates , which would then lead to a perceived decrease in the numbers of cells containing aggregates in our experiments . Our data do indicate , however , the protective effects of celastrol with respect to polyglutamine toxicity . In addition to its ability to its effects on HSF1 activity , celastrol has been found to affect other pathways in the cell , including the NF -- κB pathway -LSB- 18 , 21 , 22 , 36 -RSB- . However , our results suggest that the beneficial effects of celastrol treatment in decreasing polyglutamine toxicity are mediated through the action of the HSF1-regulated gene expression pathway , based on the finding that celastrol is not protective in Q57-YFP transfected HSF1 − / − MEF cells -LRB- Fig. 5b -RRB- . This finding also suggests that other drugs that activate HSF1 could also have potential as candidate therapeutic agents for treating polyglutamine expansion diseases . Electronic supplementary material

B O B O O O O O O O O O O B I I O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B B O O O O O O O B O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O B I I O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B O O O O O O O B O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O B O O O O O O O O O O O O O O B O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O B O O O O O O O O O O O B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O B O O O O O B O O O O O O O O B O O B O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O B O O O O O B O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O B O B O O O O O O O O O O B B O O O O O O B O O O O O O O O O O O O O B O O O O O B O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O B B O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O B O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O B O B O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O B O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B O O O O O O O O B O O B O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O B O O O O O B O O O O B O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B O O O O O B O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O B O O O O B O O B O O O O O O O B O O O O O O O O O B O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O B O O O O O O O O O O O O B O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O B O O O O O O O O B O O O O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O B O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O B O B O O O O O O O O O O O O O B O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O B O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O B O O O O O O O O O B O O O O O O O O B O O O O O O O O O O O O O O B B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B B O O O O O O B O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O B O O O O O O O B B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B B O O O O O O O O O O O O O O B O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O B O O O O O O O O O O O B O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O B O O O O O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O B O O O O O O

celastrol
hsf1
polyglutamine
aggregates
heat shock proteins
hsp70