Aminopeptidase inhibitors inhibit proliferation and induce apoptosis of K562 and STI571-resistant K562 cell lines through the MAPK and GSK-3beta pathways
Authors: Kanoko Sawafuji
Masahiro Kizaki
James D Griffin
Yasuo Ikeda
Ellen Weisberg
Yoshitaka Miyakawa
Journal: Leukemia & Lymphoma (2003)
Discipline: CLINICAL MEDICINE
Keywords: tyrosine kinase inhibitor; STI571; chronic myelogenous leukemia; drug resistance; aminopeptidase inhibitors
Affiliations: Department of Internal Medicine, Keio University School of Medicine, Tokyo 160-8582, Japan.

Abstract:  A tyrosine kinase inhibitor, STI571, has been demonstrated to be effective for the treatment of chronic myelogenous leukemia (CML). STI571 inhibits tyrosine kinase activity of ABL and induces apoptosis of CML cells. However, drug resistance develops commonly in patients with blast phase CML, and has become a significant therapeutic problem. We examined the effects of aminopeptidase inhibitors on CML cell line (K562) and a STI571-resistant subline of K562. Ubenimex and the more potent aminopeptidase inhibitor, actinonin, inhibited proliferation of both K562 cells and STI571-resistant K562 cells and also induced their apoptosis in dose- and time-dependent manners. Ubenimex and actinonin induced the activation of caspase-3, and the induction of apoptosis was inhibited by pan-caspase inhibitor, indicating this apoptosis is caspase-dependent. We found that serine phosphorylation of both MAPK and glycogen synthase kinase-3beta were suppressed by aminopeptidase inhibitors in parent K562 and STI571-resistant K562 cells. The expression level of cyclin D1 protein was also reduced by ubenimex and actinonin in both cell lines. These results indicated STI571-resistance does not confer the cross-resistance to aminopeptidase inhibitors in K562 cells and revealed the new findings of aminopeptidase inhibitor-induced intracellular signaling pathways. 

Retracted: No

PubPeer comments:
 **About** I collaborated with Dr. Jonathan Pruitt extensively from 2010-2013 when I was a new graduate student at UC Davis and he was a new postdoc (and later professor at University of Pittsburgh). I also conducted partial or full statistical analyses for two papers associated with him that both came out in 2016. In early January I began hearing about potential issues in datasets associated with Dr. Pruitt, and in response I have gone back through the data from all papers of mine where Dr. Pruitt played a role in data collection. I have been in contact with all coauthors and journal editors and steps are being taken to retract correct issue a letter of concern as needed for each paper. My goal here is to describe in detail the findings of my investigation by providing a history of the data associated with the paper, the steps Iu2019ve taken to investigate the data and the associated findings. Also, in virtually all cases these data were never public, and so I will be providing links to the original Excel files. **History of the dataset** This publication contains data collected by both myself and Dr. Pruitt. Data were collected in the summer of 2010. In this project we wanted to see how the behavioral tendencies (i.e. personalities) of crickets interacted with that of black widow spiders. Dr. Pruitt had extensive experience with spiders, so he collected the behavioral data associated with the black widows. I was working with field crickets, so I collected the data associated with the crickets. The project involved taking these individuals with known behavioral tendencies and seeing how it impacted foraging outcomes. Thus, Dr. Pruitt collected the personality data on the widow spiders, I collected the data on the field crickets, and we mutually collected data on the foraging trials. Dr. Pruitt and I worked together to build the containers for the black widow personality assays, but I never participated in the data collection for the widow spiders. I should note that the focus of these assays was not for this paper, but for the Pruitt et al. 2011 paper titled u201cIndividual-and condition-dependent effects on habitat choice and choosinessu201d in Behavioral Ecology and Sociobiology. After data were collected for that paper I used the same spiders and the behavioral data in this paper. The widow data was collected by Dr. Pruitt, and although I helped collect the spiders and saw the spiders in their assay containers, I myself never helped with the actual data collection. Eventually the data was provided to me in the form of an Excel file. I took the behavioral assay of interest from this Excel file and incorporated it into the field cricket data that I collected for the analysis. This analysis focuses on two datasets, both versions of the same black widow data collected by Dr. Pruitt. The dataset I will refer to as the u201cSmallu201d dataset is the one Dr. Pruitt provided to me when I requested it. The other, which I will refer to as the u201cLargeu201d dataset is one Dr. Pruitt provided to Dr. David Mitchell much later for the purpose of a meta analysis Dr. Mitchell was conducting. I believe this Large dataset is the actual data used in the 2011 Pruitt et. al. paper although Dr. Pruitt never confirmed this. My analysis focuses both on issues found in the Small dataset as well as comparisons between the Small and Large. **Links to Dataset** [Link to Small dataset] [Link to Large Dataset - Highlighting done by Dr. Mitchell] **Analysis of the Small Dataset** When my investigation began I only had access to the data Dr. Pruitt provided to me for this paper. The data was sent to me on October 5th, 2010 from Dr. Pruitt as it contains the behavioral assay measures of the widow spiders that I had used in the predator-prey trials. I was interested in the values relating to u2018boldness,u2019 which in this case was how close a spider would build its web near a prefered cue (cricket scent) in an non-prefered location (illuminated) vs. a non-prefered cue (no scent) in a prefered location (not illuminated). These two columns of data were in the Small dataset he provided me (columns Q R), along with additional behavioral measures that were taken and used as part of the 2011 Behavioral Ecology and Sociobiology paper. *The average of the Darkness vs. Scent columns were used in this paper, although other data were still in the Excel file* ![] I initially looked at the dataset and did not find any of the questionable patterns that were appearing in other datasets, and thus thought the dataset was fine. Again, I was there for the development of the assays, had helped collect the spiders, and had seen spiders in the assays themselves (although I did not collect any of the data). On 1 31 2020 Dr. David Mitchell contacted me as he had the full dataset used for the 2011 Behavioral Ecology and Sociobiology (hereby called the u2018Largeu2019 dataset). Dr. Pruitt sent it to him several years prior to be used in a meta analysis that Dr. Mitchell was conducting. Dr. Mitchell noted that he found a couple strange features (one or two duplicates, or values that should have ended in either .0 or .5 and didnu2019t), but overall nothing that couldnu2019t be simple data input errors. These errors are highlighted in the Large dataset. **In response I gave him the Small dataset, which when he looked at he discovered there was a formula present in one of the columns. This formula appears to generate a new series of observations from a previous set of observations. Specifically, it takes the column u2018Mu2019 and adds 1.5 to each value to create the column u2018O.u2019 This formula had been removed from the Large dataset sent to Dr. Mitchell.** *An image highlighting the formula in the Small dataset* ![] When I asked for an explanation about the formula Jonathan replied u201c [...] Lingering formula are present in some versions of data files when there contained missing values. Excel-based stats and other older stats programs don t always contend with them well. It s possible I uploaded a version with one of those present. I caught a different such instance in a data set from 2015. Many versions of files are created for graphing, visualizing, and analyzing data when one isn t using a comprehensive one-stop option (a la R). I endorse expunging those data points and seeing if it impacts any stats you ran on the data (or I ran). If they change the results in an important way that changes the interpretation, then ax the paper s. If not, then consider an addendum. It s important not to throw the baby out with the bath water. [...] u201d **Comparison of the Large and Small Dataset** When later looking at the Large dataset I discovered a few additional issues. First, the length of the Large dataset Jonathan provided to me differs from that he provided to Dr. Mitchell. The Small dataset contains 44 observations while the Large contains 52. They appear to differ in that individuals 45-52 are removed from the dataset provided to me. The Large dataset had additional measures included that were not in the dataset given to me. This Large dataset does not contain the formula that makes a new column of observations, although the data generated from the formula do still exist (Column P is column N + 1.5). In this dataset I count 16 observations of the 52 that do not fit the + 1.5 formula. Digging into these 16 cases I find that some of the values donu2019t match across the Small and Large datasets in the Darkness assays. Each spider was supposed to be run through the Darkness assay six times. Initially I noticed that some of the values for the column with the formula differed, but looking more closely it appears that several of the six indiivdual measures for spiders differ across the datasets in the Darkness measure. For example, IND 6 in both datasets have some different measures for the Darkness assay (the SD columns in the Large dataset), but the same six measures for the Cricket scent assay (SC columns in the Large dataset). *Highlight of Individual 6 from the Small dataset* ![] *Highlight of Individual 6 from the Small dataset - Note that the SD measures here differ from the Darkness measures above, although the SC Scent measures are the same.* ![] The same pattern applies for individuals 9, 16, 17, 18, 19, 33, and 41. Given the different lengths of the dataset it could be that the ID numbers were recoded between the two (they re both continuous with no missingness, and it appears the small one just had the end set of individuals removed), but there are unique features in the ID matches between the two datasets that suggest this is not the case. For example, the first cricket scent measure for individual 16 in both datasets is 4.6. This value actually shouldn t occur as things were measured to the nearest 1 2 centimeter. This unique input error occurs both places. Similarly, individual 19 is missing the last two cricket scent measures in both datasets. So, these do appear to be the same individuals in both datasets, but somehow the individuals in the Large dataset have some different measurements for the Darkness assay. I canu2019t figure out how such selective differences would occur through an excel error. I can t make the above comparison for individuals 45-52 as they were removed from the data provided to me that contained the formula (making the Small dataset). But, none of those individuals match the + 1.5 for the observations in question. **Conclusion** Although the paper here didnu2019t use the column of data that was generated by a formula, it did use two columns that come from that dataset (the average of the two Darkness vs. Scent columns). There is no reason that such a formula should ever exist in a dataset that is supposed to contain individual behavioral measures, and I have yet to receive a satisfactory explanation as to why it was in there. This issue, mixed with the other discrepancies between the Small and Large datasets has caused me to be cautious of the rest of the data contained in the Small datatset, including the two columns of data used in the paper. ,