Reproducibility in Cancer Biology: Pseudogenes, RNAs and new reproducibility norms

The partial success of a study to reproduce experiments that linked pseudogenes and cancer proves that understanding RNA networks is more complicated than expected.
  1. George A Calin  Is a corresponding author
  1. Translational Molecular Pathology Department, University of Texas MD Anderson Cancer Center, United States
  2. Center for RNA Interference and Non-Coding RNAs, University of Texas MD Anderson Cancer Center, United States

Of all the molecules involved in the flow of genetic information within any biological system, RNA is the oldest (Crick, 1970). This means that RNA molecules can interact with other RNAs and with all the other molecules that appeared later in evolution, including DNA molecules, proteins and lipids (Fabbri et al., 2019). RNA further evolved into coding RNA molecules (which are translated into proteins) and non-coding RNA molecules (which are not translated, but can still perform a number of other roles within cells; Pang et al., 2006). The most widely studied non-coding RNAs are the short transcripts called microRNAs that can silence a molecule of messenger RNA and prevent it from being translated into a protein. Relatively little studied, on the other hand, are non-coding RNAs called pseudogenes: a pseudogene is an RNA molecule that has been transcribed from a DNA segment that resembles a protein-coding gene but, for various reasons, this DNA is never expressed as a protein.

In 2010 researchers at the Beth Israel Deaconess Medical Center, Harvard University and the Memorial Sloan Kettering Cancer Center reported some surprising results about a pseudogene that is related to PTEN, a gene that codes for a protein that can suppress tumors (Poliseno et al., 2010). Mutations in this gene are linked to a number of cancers. Poliseno et al. reported that the pseudogene, which is called PTENP1, is biologically active and can regulate the levels of PTEN in cells via the direct interaction with a number of different microRNAs.

This report changed the dogmatic view of pseudogenes as relicts of genomic evolution: rather, by retaining multiple sites that can bind microRNAs, pseudogenes can act as decoys for their functional counterpart. The results of Poliseno et al. therefore provided support for a theory in which RNAs are regulated by other RNAs (Salmena et al., 2011; please see Thomson and Dinger (2016) for a balanced description of this theory, which is called the 'competing endogenous RNA theory'). Moreover, the results in the 2010 paper showed that pseudogenes can influence genes that are involved in cancer.

In 2015, as part of the Reproducibility Project: Cancer Biology, Khan et al. published a Registered Report which explained in detail how they would seek to replicate four of these experiments (Khan et al., 2015a; somewhat unusually, this report has been corrected twice: Khan et al. (2015b); Khan et al. (2015c). The results of these experiments have now been published as a Replication Study (Kerwin et al., 2020). As with a number of other Replication Studies in this project, some of the original results have been reproduced and some have not. Moreover, as we will describe below, the Replication Study does not contain data for one of the four experiments (although these data will be made available at Unfortunately, this was probably the most important of the experiments. First, however, we will discuss the experiments for which data are reported.

Poliseno et al. reported that the depletion of PTEN and/or PTENP1 increased the proliferation of DU145 prostate cancer cells, compared to administration of non-targeting siRNA, by an amount that was statistically significant (Figure 2F in the 2010 paper). The Replication Study also reports a similar increase in proliferation, and while it is not statistically significant, it supports the idea that pseudogenes can have a functional role in human cancers. Moreover, a study published by researchers at the University of Michigan in 2012 confirmed that transcribed pseudogenes are an important contributor in the transcriptional landscape of cancer cells (Kalyana-Sundaram et al., 2012).

The original study reported that overexpression of PTEN 3’UTR increased PTENP1 levels in DU145 cells (Figure 4A), whereas the Replication Study reports that it does not. As the level of the 3’UTR expression was not determined in either study, it is not possible to compare the amount of 3’UTR molecules used by the two groups, so it is difficult to make meaningful comparisons. However, the original study and the Replication Study both found that overexpression of PTEN 3’UTR led to a statistically significant decrease in the proliferation of DU145 cells compared to controls.

In the original study Poliseno et al. reported that two microRNAs – miR-19b and miR-20a – suppress the transcription of both PTEN and PTENP1 in DU145 prostate cancer cells (Figure 1D), and that the depletion of PTEN or PTENP1 led to a statistically significant reduction in the corresponding pseudogene or gene (Figure 2G). Neither of these effects were seen in the Replication Study. There are many possible explanations for this. For example, although both studies used DU145 prostate cancer cells, they did not come from the same batch, so there could be significant genetic differences between them: see Andor et al. (2020) for more on cell lines acquiring mutations during cell cultures. Furthermore, one of the techniques used in both studies – quantitative real-time PCR – depends strongly on the reagents and operating procedures used in the experiments. Indeed, there are no widely accepted standard operating procedures for this technique, despite over a decade of efforts to establish such procedures (Willems et al., 2008; Schwarzenbach et al., 2015).

What are the take-home messages from this Replication Study? One is the importance of fruitful communication between the laboratory that did the initial experiments and the lab trying to repeat them. The lack of such communication – which should extend to the exchange of protocols and reagents – was the reason why the experiments involving microRNAs could not be reproduced. The original paper did not give catalogue numbers for these reagents, so the wrong microRNA reagents were used in the Replication Study. The introduction of reporting standards at many journals means that this is less likely to be an issue for more recent papers. Another take-home message is that it is finally time for the research community to make raw data obtained with quantitative real-time PCR openly available for papers that rely on such data. This would be of great benefit to any group exploring the expression of the same gene/pseudogene/non-coding RNA in the same cell line or tissue type.

The true power of the Reproducibility Project is not restricted to what it can tell us about the robustness of papers in the field of cancer biology: rather, it should make researchers in the field – and the wider scientific community – realize that we can and should establish new standards and norms to make data freely available and comparable.


George A Calin was the Reviewing Editor for the Registered Report (Khan et al., 2015a) and the Replication Study (Kerwin et al., 2020).


Article and author information

Author details

  1. George A Calin

    George A Calin is in the Translational Molecular Pathology Department and the Center for RNA Interference and Non-Coding RNAs, University of Texas MD Anderson Cancer Center, Houston, Texas, United States

    For correspondence
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7427-0578

Publication history

  1. Version of Record published: April 21, 2020 (version 1)


© 2020, Calin

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.


  • 915
    Page views
  • 72
  • 2

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. George A Calin
Reproducibility in Cancer Biology: Pseudogenes, RNAs and new reproducibility norms
eLife 9:e56397.

Further reading

    1. Cancer Biology
    Laura M Sipe et al.
    Research Article

    Bariatric surgery is becoming more prevalent as a sustainable weight loss approach, with vertical sleeve gastrectomy (VSG) being the first line of surgical intervention. We and others have shown that obesity exacerbates tumor growth while diet-induced weight loss impairs obesity-driven progression. It remains unknown how bariatric surgery-induced weight loss impacts cancer progression or alters responses to therapy. Using a pre-clinical model of diet induced obesity followed by VSG or diet-induced weight loss, breast cancer progression and immune checkpoint blockade therapy was investigated. Weight loss by bariatric surgery or weight matched dietary intervention before tumor engraftment protected against obesity-exacerbated tumor progression. However, VSG was not as effective as dietary intervention in reducing tumor burden despite achieving a similar extent of weight and adiposity loss. Circulating leptin did not associate with changes in tumor burden, however circulating IL-6 was elevated in mice after VSG. Uniquely, tumors in mice that received VSG displayed elevated inflammation and immune checkpoint ligand PD-L1+ myeloid and non-immune cells. Further, mice that received VSG had reduced tumor T lymphocytes and markers of cytolysis suggesting an ineffective anti-tumor microenvironment. VSG-associated elevation of PD-L1 prompted us to next investigate the efficacy of immune checkpoint blockade in lean, obese, and formerly obese mice that lost weight by VSG or weight matched controls. While obese mice were resistant to immune checkpoint blockade, anti-PD-L1 potently impaired tumor progression after VSG through improved anti-tumor immunity. Thus, in formerly obese mice, surgical weight loss followed by immunotherapy reduced breast cancer burden. Last, we compared transcriptomic changes in adipose tissue after bariatric surgery from both patients and mouse models that revealed a conserved bariatric surgery associated weight loss signature (BSAS). Importantly, BSAS significantly associated with decreased tumor volume. Our findings demonstrate conserved impacts of obesity and bariatric surgery-induced weight loss pathways associated with breast cancer progression.

    1. Cancer Biology
    2. Cell Biology
    Johnny M Tkach et al.
    Research Article

    Centrosomes act as the main microtubule organizing center (MTOC) in metazoans. Centrosome number is tightly regulated by limiting centriole duplication to a single round per cell cycle. This control is achieved by multiple mechanisms, including the regulation of the protein kinase PLK4, the most upstream facilitator of centriole duplication. Altered centrosome numbers in mouse and human cells cause p53-dependent growth arrest through poorly defined mechanisms. Recent work has shown that the E3 ligase TRIM37 is required for cell cycle arrest in acentrosomal cells. To gain additional insights into this process, we undertook a series of genome-wide CRISPR/Cas9 screens to identify factors important for growth arrest triggered by treatment with centrinone B, a selective PLK4 inhibitor. We found that TRIM37 is a key mediator of growth arrest after partial or full PLK4 inhibition. Interestingly, PLK4 cellular mobility decreased in a dose-dependent manner after centrinone B treatment. In contrast to recent work, we found that growth arrest after PLK4 inhibition correlated better with PLK4 activity than with mitotic length or centrosome number. These data provide insights into the global response to changes in centrosome number and PLK4 activity and extend the role for TRIM37 in regulating the abundance, localization, and function of centrosome proteins.