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Defining the critical hurdles in cancer immunotherapy

Overview of attention for article published in Journal of Translational Medicine, December 2011
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (85th percentile)

Mentioned by

10 tweeters
1 Facebook page
1 Google+ user


134 Dimensions

Readers on

263 Mendeley
1 CiteULike
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Defining the critical hurdles in cancer immunotherapy
Published in
Journal of Translational Medicine, December 2011
DOI 10.1186/1479-5876-9-214
Pubmed ID

Bernard A Fox, Dolores J Schendel, Lisa H Butterfield, Steinar Aamdal, James P Allison, Paolo Antonio Ascierto, Michael B Atkins, Jirina Bartunkova, Lothar Bergmann, Neil Berinstein, Cristina C Bonorino, Ernest Borden, Jonathan L Bramson, Cedrik M Britten, Xuetao Cao, William E Carson, Alfred E Chang, Dainius Characiejus, A Raja Choudhury, George Coukos, Tanja de Gruijl, Robert O Dillman, Harry Dolstra, Glenn Dranoff, Lindy G Durrant, James H Finke, Jerome Galon, Jared A Gollob, Cécile Gouttefangeas, Fabio Grizzi, Michele Guida, Leif Håkansson, Kristen Hege, Ronald B Herberman, F Stephen Hodi, Axel Hoos, Christoph Huber, Patrick Hwu, Kohzoh Imai, Elizabeth M Jaffee, Sylvia Janetzki, Carl H June, Pawel Kalinski, Howard L Kaufman, Koji Kawakami, Yutaka Kawakami, Ulrich Keilholtz, Samir N Khleif, Rolf Kiessling, Beatrix Kotlan, Guido Kroemer, Rejean Lapointe, Hyam I Levitsky, Michael T Lotze, Cristina Maccalli, Michele Maio, Jens-Peter Marschner, Michael J Mastrangelo, Giuseppe Masucci, Ignacio Melero, Cornelius Melief, William J Murphy, Brad Nelson, Andrea Nicolini, Michael I Nishimura, Kunle Odunsi, Pamela S Ohashi, Jill O'Donnell-Tormey, Lloyd J Old, Christian Ottensmeier, Michael Papamichail, Giorgio Parmiani, Graham Pawelec, Enrico Proietti, Shukui Qin, Robert Rees, Antoni Ribas, Ruggero Ridolfi, Gerd Ritter, Licia Rivoltini, Pedro J Romero, Mohamed L Salem, Rik J Scheper, Barbara Seliger, Padmanee Sharma, Hiroshi Shiku, Harpreet Singh-Jasuja, Wenru Song, Per Thor Straten, Hideaki Tahara, Zhigang Tian, Sjoerd H van Der Burg, Paul von Hoegen, Ena Wang, Marij JP Welters, Hauke Winter, Tara Withington, Jedd D Wolchok, Weihua Xiao, Laurence Zitvogel, Heinz Zwierzina, Francesco M Marincola, Thomas F Gajewski, Jon M Wigginton, Mary L Disis


Scientific discoveries that provide strong evidence of antitumor effects in preclinical models often encounter significant delays before being tested in patients with cancer. While some of these delays have a scientific basis, others do not. We need to do better. Innovative strategies need to move into early stage clinical trials as quickly as it is safe, and if successful, these therapies should efficiently obtain regulatory approval and widespread clinical application. In late 2009 and 2010 the Society for Immunotherapy of Cancer (SITC), convened an "Immunotherapy Summit" with representatives from immunotherapy organizations representing Europe, Japan, China and North America to discuss collaborations to improve development and delivery of cancer immunotherapy. One of the concepts raised by SITC and defined as critical by all parties was the need to identify hurdles that impede effective translation of cancer immunotherapy. With consensus on these hurdles, international working groups could be developed to make recommendations vetted by the participating organizations. These recommendations could then be considered by regulatory bodies, governmental and private funding agencies, pharmaceutical companies and academic institutions to facilitate changes necessary to accelerate clinical translation of novel immune-based cancer therapies. The critical hurdles identified by representatives of the collaborating organizations, now organized as the World Immunotherapy Council, are presented and discussed in this report. Some of the identified hurdles impede all investigators; others hinder investigators only in certain regions or institutions or are more relevant to specific types of immunotherapy or first-in-humans studies. Each of these hurdles can significantly delay clinical translation of promising advances in immunotherapy yet if overcome, have the potential to improve outcomes of patients with cancer.

Twitter Demographics

The data shown below were collected from the profiles of 10 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 263 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 2%
France 1 <1%
Netherlands 1 <1%
Canada 1 <1%
United Kingdom 1 <1%
Unknown 254 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 73 28%
Student > Ph. D. Student 43 16%
Student > Bachelor 24 9%
Student > Master 23 9%
Other 19 7%
Other 50 19%
Unknown 31 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 69 26%
Medicine and Dentistry 66 25%
Immunology and Microbiology 33 13%
Biochemistry, Genetics and Molecular Biology 24 9%
Engineering 8 3%
Other 29 11%
Unknown 34 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 8. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 13 November 2020.
All research outputs
of 20,419,783 outputs
Outputs from Journal of Translational Medicine
of 3,564 outputs
Outputs of similar age
of 242,202 outputs
Outputs of similar age from Journal of Translational Medicine
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Altmetric has tracked 20,419,783 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,564 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.9. This one has done well, scoring higher than 84% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 242,202 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 85% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them