Chris Armit

::: {.wp-block-image}
<figure class="alignleft size-large is-resized">
<img
src="http://gigasciencejournal.com/blog/wp-content/uploads/2022/03/Tom.png"
class="wp-image-4300" loading="lazy" decoding="async"
srcset="http://gigasciencejournal.com/blog/wp-content/uploads/2022/03/Tom.png 220w, http://gigasciencejournal.com/blog/wp-content/uploads/2022/03/Tom-150x150.png 150w"
sizes="(max-width: 220px) 100vw, 220px" width="220" height="220"
alt="Intensive Care database author Tom Edinburgh" />
</figure>
:::

*We have a Q&A with author Tom Edinburgh from the University of
Cambridge on his [new GigaByte
paper](https://doi.org/10.46471/gigabyte.45) presenting Sepsis-3
criteria in AmsterdamUMCdb, which is one of the largest freely
accessible Intensive Care database in Europe. As a journal that promotes
transparency and openness, we've [previously
written](http://gigasciencejournal.com/blog/controlled-access-data/){rel="noreferrer noopener"
target="_blank"} about the challenges of peer reviewing clinical data,
and how our named [open peer
review](https://gigabytejournal.com/reviewer-information) provides
opportunities in opening up access of controlled access datasets. Here
we provide another example of the peer reviewing of de-identified health
data, with an additional layer of complexity that reviewers can overcome
by completing a course on human subjects research. The authors were keen
to be able to share as many outputs of this project as they could, being
big reproducible research (see contributor to this work and GigaByte
Editorial Board Member Stephen Eglen's previous Q&A on his early
attempts at ["push button papers" using
Knitr](http://gigasciencejournal.com/blog/carmen-reproducible-research-and-push-button-papers/){rel="noreferrer noopener"
target="_blank"} and CodeCheck certified [reproducibility
certificates](http://gigasciencejournal.com/blog/codecheck-certificate/){rel="noreferrer noopener"
target="_blank"}). In this post Tom explains this process and the key
advantages of the new clinical definition of Sepsis-3 over previous
definitions of Sepsis.*

Sepsis is a life-threatening condition and is characterised by organ
dysfunction. The dysregulated host response to infection is a major
factor in the aetiology of sepsis, and Sepsis-3 includes a revised
definition of sepsis based on a scoring system that utilises Sequential
Organ Failure Assessment (SOFA) across six domains including
respiratory, neurological, cardiovascular, liver, coagulation and renal
systems.

I was the Curator and Reviewer for this [*GigaByte* Technical Release
manuscript](https://doi.org/10.46471/gigabyte.45), and it was
interesting to explore how one gains access to the de-identified health
data used in this study. [Amsterdam University Medical Centers Database
(AmsterdamUMCdb)](https://amsterdammedicaldatascience.nl/#amsterdamumcdb)
contains de-identified health data related to tens of thousands of
intensive care unit admissions, including demographics, vital signs,
laboratory tests and medications. The database, although de-identified,
still contains detailed information regarding the clinical care of
patients, so must be treated with appropriate care and respect and
cannot be shared without permission.

To gain access to AmsterdamUMCdb, one must first complete the Data or
Specimens Only Research (DSOR) course from CITI. This course introduces
some of the core issues in Human Subjects research, including
deidentification, informed consent, Institutional Review Board (IRB)
policy, and conflict of interest (COI), and there is additionally advice
on when an IRB member should recuse themself from voting on a study that
is under review. The background on Ethics is especially interesting and
highlighted the need for the publication of the 1974 [Belmont
Report](https://www.hhs.gov/ohrp/regulations-and-policy/belmont-report/index.html){target="_blank"
rel="noreferrer noopener"} for the Protection of Human Subjects of
Biomedical and Behavioral Research.

::: {.wp-block-image}
<figure class="alignleft size-large is-resized">
<img
src="http://gigasciencejournal.com/blog/wp-content/uploads/2022/03/logo_amds.png"
class="wp-image-4294" loading="lazy" decoding="async"
srcset="http://gigasciencejournal.com/blog/wp-content/uploads/2022/03/logo_amds.png 512w, http://gigasciencejournal.com/blog/wp-content/uploads/2022/03/logo_amds-300x300.png 300w, http://gigasciencejournal.com/blog/wp-content/uploads/2022/03/logo_amds-150x150.png 150w"
sizes="(max-width: 380px) 100vw, 380px" width="380" height="380"
alt="Amsterdam Medical Data Science Logo" />
</figure>
:::

Once I completed the DSOR course I was able to submit my Access Request
Form and End User License Agreement for AmsterdamUMCdb. This application
needs to be counter-signed by an intensivist, i.e. a health professional
who specialises in the care of critically ill patients, and who is the
named reference on the application from. I am grateful to [Dr Ari
Ercole](http://anaesthetics.medschl.cam.ac.uk/staff/ari-ercole/){target="_blank"
rel="noreferrer noopener"} who, as an intensivist and one of the
co-authors of the *GigaByte* manuscript, very kindly provided this
reference in support of my application. Access to AmsterdamUMCdb was
relatively straightforward, and the Data Archiving and Networked
Services (DANS) based in the Netherlands were very responsive and were
able to send me a link where I could download the AmsterdamUMCdb
archive.

Here, in one of our long running [author
Q&A's](http://gigasciencejournal.com/blog/tag/qa/){target="_blank"
rel="noreferrer noopener"} we talk to first author Tom Edinburgh, a PhD
student from the University of Cambridge about this work.

***Can you tell us a bit about the Amsterdam University Medical Centers
Database (AmsterdamUMCdb)? What is it and how does it work? Did your
analysis of this massive cohort uncover any surprising findings?***

Intensive care is home to perhaps the most data-dense clinical
environment, with continuous multimodality monitoring (which includes
demographics, physiology, treatments and drugs, lab values and scans) an
essential component in patient care. This creates a real challenge for
data-sharing, because the level of detail raises privacy, ethical and
legal concerns that need to be balanced against the potential research
benefits, and databases must undergo a comprehensive risk-based
de-identification process before becoming freely-accessible. Until
recently, the prominent large-scale intensive care unit (ICU) databases
were limited to cohorts from the US, and differences in demographics,
treatments and resources limits the ability to generalise knowledge to
other populations. Amsterdam University Medical Centers Database
(AmsterdamUMCdb) is a [recent European ICU
database](http://dx.doi.org/10.1097/CCM.0000000000004916), which is
compliant with both the U.S. Health Insurance Portability and
Accountability Act (HIPAA) and the European General Data Protection
Regulation (GDPR) and is freely accessible. GDPR compliance and the
demonstration of public acceptability for such projects was a landmark
step forwards for this kind of work in the EU. It contains approximately
1 billion data points from \>20,000 critically ill patients admitted to
Amsterdam UMC between 2003 and 2016, with the records having passed
through a robust risk-based de-identification process. The different
modalities to the data are combined in a data-lake structure, linked via
anonymised identifiers, and is accompanied by a [GitHub
repository](https://github.com/tedinburgh/sepsis3-amsterdamumcdb)
containing detailed resources for users.

***The [new paper in GigaByte](https://doi.org/10.46471/gigabyte.45)
presents the code for implementing the Sepsis-3 definition. What exactly
does this do, and why did you decide to open source it?***

Worldwide, sepsis is a huge killer and a key reason for ICU admission.
As such, it has always been, and will likely always continue to be, an
important area for ICU research. The huge heterogeneity in sepsis
patients makes a Big Data observational database approach very
attractive. However, this is always fundamentally going to depend on
operationalising a consistent definition of sepsis, which isn't an easy
task. This implementation of the Sepsis-3 definition has several key
components: First, we must calculate the Sequential Organ Failure
Assessment (SOFA) scores, which are a marker of the severity of the
patient condition. This is made up of a number of sub-scores, each
graded on an integer scale from 0 to 4, relating to the cardiovascular,
respiratory and central nervous systems, as well as coagulation, liver
function and renal function. A new organ dysfunction is defined as an
increase in total SOFA score of at least 2 points. For the Sepsis-3
criteria to be met, this must be accompanied by acute infection, which
is operationalised by non-prophylactic antibiotic escalation (which may
be an addition to the antibiotics administered or a switch to stronger
antibiotics). We also restructured the previous 'diagnosed at admission'
sepsis definition currently employed in the AmsterdamUMCdb repository,
in order to make direct comparison between these definitions. We're
passionate about reproducibility and open science, and making our work
on this open-source is a small effort in the right direction towards
this goal, allowing users not only to use the Sepsis-3 definition within
AmsterdamUMCdb but also to examine more closely the underlying
computations.

***You say in the manuscript that the incidence of Sepsis at admission
is rarely documented consistently. What are the key advantages of
Sepsis-3 clinical criteria over previous definitions of Sepsis?***

Sepsis has a broad umbrella definition ('a life-threatening organ
dysfunction caused by a dysregulated host response to infection') and
it's even harder to operationalise that definition in a clinical
setting. It's often not considered the 'primary' reason for admission
even when identified during admission, and so may not always be recorded
consistently. Whilst there is debate within the clinical care community
about the shifting definition of sepsis, the Sepsis-3 definition
addresses concerns that the [previous 'SIRS'
definition](http://dx.doi.org/10.1001/jama.2016.0287) is not necessarily
indicative of a dysregulated life-threatening response ('poor
discriminant validity'). Sepsis-3 was proposed as a more data-driven
definition and is accompanied by a bedside clinical score for rapid
identification that can be consistently applied across ICU centres.

***[AmsterdamUMCdb](https://amsterdammedicaldatascience.nl/#amsterdamumcdb)
archives clinical data from a 13-year period between 2003 and 2016. Are
there plans to extend this resource to include more recent Intensive
Care Unit patient data, for example, patient data from the current COVID
pandemic?***

Good question! A COVID-19 intensive care unit database is actually
something that is already largely in place, and it's a very impressive
feat that Amsterdam UMC and partner company Pacmed have achieved with
this, [because they have integrated patient
data](https://doi.org/10.1007/s00134-021-06361-x) from 35 intensive care
units in Netherlands. This nationwide data-sharing collaboration and
processing pipeline has created the first EHR database with
full-admission data from COVID-19 patients admitted to ICU, with \>400
million data points on over 46,000 parameters from more than 1500
critically ill patients. More details about this can be found at
<https://amsterdammedicaldatascience.nl>. Access is necessarily more
tightly controlled than for AmsterdamUMCdb, as the smaller number of
patients increases the risks of re-identification. However the existence
of this COVID-19 dataset was actually one of the key motivators for the
work detailed in our manuscript, as we sought a respiratory sepsis
cohort for comparison with a similar COVID-19 cohort from the DDW in an
ongoing research project.

***What challenges did you have with sharing [this
code](https://github.com/tedinburgh/sepsis3-amsterdamumcdb)? As we have
named, open peer review, how did you find this process, and how did it
assist review of the code and data?***

We're very grateful to the reviewers, Chris Armit and Tom Pollard, for
their feedback, including comments about code readability and usability
that we can take forward more generally in future projects. The
suggestions they made helped us to go back over the code with an eye to
improving its clarity. In particular, whilst it is non-trivial to avoid
hardcoding, given the codebase is often reliant on line-by-line
transformations of unique variables, we did introduce some capability
for command-line argument parsing. Publishing this manuscript in an open
science journal, where results are independently verified in peer
review, would have been trickier without coordination with the editorial
team and the open peer review, since there are necessarily some access
requirements for AmsterdamUMCdb (these requirements and the database
set-up are straightforward but potentially time-consuming for a one-off
use of the database during review). The *GigaByte* review format is very
structured, which is helpful for authors (and I imagine reviewers as
well).

***You have pointed out that you would welcome any contribution to this
codebase from the wider user community. What sort of things would you
like to see undertaken?***

First and foremost, we hope this is a useful resource for researchers
who are working with
[AmsterdamUMCdb](https://amsterdammedicaldatascience.nl/#amsterdamumcdb),
which, as mentioned, is one of the largest freely-available critical
care databases. As an example, having identified ICU data sharing as a
key priority and supported the development and release of
AmsterdamUMCdb, the European Society of Intensive Care Medicine have
already hosted multidisciplinary-team datathons (and planned future
ones) to investigate clinical questions using AmsterdamUMCdb. Some of
the technicalities of coding the definition of Sepsis-3 may be
challenging for clinicians with an interest in data science, and having
access to scripts such as this may reduce some of the technical barriers
and allow researchers to more easily define patient cohorts for their
investigations. At the same time, we definitely welcome feedback or
improvements to the code, and to that end users can create an issue on
the GitHub repository.

Apart from this, it's perhaps not straightforward to extend the
[codebase](https://github.com/tedinburgh/sepsis3-amsterdamumcdb)
directly, as ultimately it's a single immutable transformation of the
dataset into something clinically useful. Large-scale intensive care
databases, at the current time, are distinct in terms of structure,
parameters available, and even default language of any plain-text, so
it's an incredibly challenging task to map EHRs from multiple hospitals
to a single consistent data model, when these hospitals may have
different data recording and storing systems. This is without factoring
in differences in protocols and treatments which are important to
consider in this kind of work; for instance, we needed to identify
antibiotic administration from Amsterdam UMC's routine practice of
selective digestive decontamination. However, one benefit of making this
code open-access is that it can potentially provide a starting point for
other researchers to encode the Sepsis-3 definitions and identify
patient cohorts in other similar databases.

**Further Reading** 
Tom Edinburgh, Stephen J. Eglen, Patrick Thoral, Paul Elbers, Ari
Ercole, Sepsis-3 criteria in AmsterdamUMCdb: open-source code
implementation, *GigaByte*, 1, 2022
<https://doi.org/10.46471/gigabyte.45>

The post [Opening access and exploring sepsis in an Intensive Care
database. Q&A with Tom
Edinburgh](http://gigasciencejournal.com/blog/opening-access-and-exploring-sepsis-in-an-intensive-care-database-qa-with-tom-edinburgh/){rel="nofollow"}
appeared first on
[GigaBlog](http://gigasciencejournal.com/blog){rel="nofollow"}.

We have a Q&amp;A with author Tom Edinburgh from the University of Cambridge on his new GigaByte paper presenting Sepsis-3 criteria in AmsterdamUMCdb, which is one of the largest freely accessible Intensive Care database in Europe.

Opening access and exploring sepsis in an  Intensive Care database. Q&amp;A with Tom Edinburgh

Scott Edmunds

*![Controlled Access Data Q&A
author](http://gigasciencejournal.com/blog/wp-content/uploads/2020/11/3366818-300x300.jpg){.alignright
.wp-image-3607 loading="lazy" decoding="async" width="199" height="199"
srcset="http://gigasciencejournal.com/blog/wp-content/uploads/2020/11/3366818-300x300.jpg 300w, http://gigasciencejournal.com/blog/wp-content/uploads/2020/11/3366818-150x150.jpg 150w, http://gigasciencejournal.com/blog/wp-content/uploads/2020/11/3366818.jpg 412w"
sizes="(max-width: 199px) 100vw, 199px"}We have a Q&A with author
Matthieu Foll from the WHO International Agency for Research on Cancer*
(IARC) *on his [new GigaScience
paper](https://doi.org/10.1093/gigascience/giaa112) presenting
multi-omic data from rare lung neuroendocrine neoplasms and his
experience having the precious* *Controlled Access data peer reviewed by
named peer reviewers.*

As a journal focused on reproducibility of research *GigaScience* has a
strict open-science policy, meaning access to the data and software
supporting our papers needing to be available to peer reviewers and
readers. This is usually relatively straightforward with studies on
microbes, animals, and plants ([even when 13TB in
size](http://gigasciencejournal.com/blog/publish-data-fight-world-hunger/)),
but for human research if data is potentially identifiable there are
legal and ethical challenges making this more difficult to do. A lot of
human sequencing data is now hosted in controlled access repositories
such as [dbGAP](https://www.ncbi.nlm.nih.gov/gap/) and the
[EGA](https://www.ebi.ac.uk/ega/), where researchers can gain access the
data after applying to a Data Access Committee (DAC) and meeting
guidelines relating to the research they wish to carry out. In the past
when working with papers containing this type of data we would try to
make the brokering process easier for readers by providing the data
access forms alongside links to the repository (see [this example in
GigaDB](http://dx.doi.org/10.5524/100181)). To aid reviewers we would
usually encourage the authors to provide a representative anonymized
dataset, or provide a link to public datasets that could be tested in a
similar manner, but this is not the same as having hands-on access to
the actual real-world data supporting the results of the study.

Newly [published in
*GigaScience*](https://doi.org/10.1093/gigascience/giaa112) we see an
example of the benefits of our mandatory Open Peer Review policy, where
a DAC allowed qualified peer reviewers access to inspect the data in
their repository as they were named and met the criteria of access (read
the actual review
[here](https://oup.silverchair-cdn.com/oup/backfile/Content_public/Journal/gigascience/9/11/10.1093_gigascience_giaa112/1/giaa112_reviewer_1_report_original_submission.pdf?Expires=1607464350&Signature=1wT3iZyFxaxS7ut0D2C5XDcXFDLs2OgJpJHTAJSEccRqVxqsfl511wY1cKDqRgNbw0SIks4xqjKANbSUP3pcAnJ97HcPxD3QfP1NjBAFgFOmZoe8iO6SOumY5thJIvUqrm4HdFCHBdAv8oWWWWsSTJ1T9W~S03IBiIGavSxCuU~8x2p15z851iWjB5pXfxNMqHjhsmiY3wtBVjhTakf1NKeh6dKDHTuNxL4FwM6AKbwY5BzhHctGbaDoooP2PkCHxB4i0aaGEXM66jPNYF~D7AFtmNq7Y1kFY3ipGdwqsR4~pgbaRZQjKRLuwdNi7Jzpr06fEU9iggf4xwdt9RMS4A__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA)).
We've written in [this
blog](http://gigasciencejournal.com/blog/peerrevwk17-update-peer-review-week-goes-transparent-and-interactive/)
and [in Editorials](https://doi.org/10.1186/2047-217X-2-1) of the
benefits of Open Peer Review and this is another example supporting why
we and a growing number of other journals (e.g.
[PLOS](https://theplosblog.plos.org/2019/05/plos-journals-now-open-for-published-peer-review/)
and [Nature](https://www.nature.com/articles/d41586-020-00309-9)
journals) have moved towards this fairer and more transparent model. To
give an author's insight we provide one of our long running [author Q&A
articles](http://gigasciencejournal.com/blog/tag/qa/) with lead author
[Matthieu Foll](https://www.iarc.fr/staff_member/matthieu-foll/), who is
a Bioinformatics Scientist in the Genetic Cancer Susceptibility Group of
IARC in Lyon.

**Your paper is a [Data Note
describing](https://doi.org/10.1093/gigascience/giaa112) a multi-omic
dataset from rare lung neuroendocrine neoplasms, so why did you produce
this data?**

The molecular characterization of tumors has revolutionized our
understanding of cancer biology, how cancer is classified, and how it is
treated in the clinic. Large national and international initiatives have
performed multi-omics characterization of most common cancers, but
several rare cancer types or subtypes have been omitted from these
studies, even if they collectively account for \~25--30% of all cancer
diagnoses and 25% of cancer deaths. This lack of biological knowledge
and consequently, limited therapeutic opportunities translates in a
five-year survival on average worse for rare cancers (47%) than common
cancers (65%). In this context, we have established the Rare Cancers
Genomics initiative
([www.rarecancersgenomics.co](http://www.rarecancersgenomics.co/){rel="noreferrer noopener"
target="_blank"}[m](http://www.rarecancersgenomics.com/){rel="noreferrer noopener"
target="_blank"}): an international multidisciplinary open-science
effort to shed light on the molecular characteristics of rare cancers,
to understand the etiology and carcinogenesis processes, to ultimately
improve their clinical management and consequently, their prognosis. In
this context we have [recently
produced](https://www.nature.com/articles/s41467-019-11276-9) the first
multi-omics data of the rare pulmonary carcinoids, that was the missing
piece for a complete molecular characterization of lung neuroendocrine
neoplasms (representing approximately 25% of all lung cancers), which
also include the poorly differentiated and highly aggressive lung
neuroendocrine carcinomas -- *i.e.*, small-cell lung cancer and
large-cell neuroendocrine carcinoma. We have integrated this dataset
with five previously published datasets to generate the first
comprehensive molecular map of lung neuroendocrine neoplasms.

**What are the challenges of working with such rare tumours, and how
does this rarity effect the value and what others can do with their
data?**

One of the main limitations we are facing is the lack of available
biological material suitable for molecular studies due to the rarity of
these diseases. Using fresh frozen tissue samples is preferred for
molecular studies because of the higher quality of DNA/RNA they provide,
while Formalin-Fixed, Paraffin-Embedded (FFPE) remains the tissue
preservation method of choice in clinical settings. However, FFPE tissue
processing and storage result in degraded DNA and RNA, limiting gene
detection and introducing sequencing artefacts. As a consequence, rare
cancer studies are usually based on small series of samples and are
therefore underpowered to draw meaningful conclusions. It is therefore
of the utmost importance to provide the full spectrum of data and tools
to allow other researchers to re-use our data together with a future
series of samples. 

**What issues did you have with sharing this data? As we have named,
open peer review, how did you find this process and how did it assist
review of the data?**

Human genomic data is regarded as personal data and can generally only
be shared with controlled-access, where only researchers who apply for
and get permission will be able to access it after signing a data access
agreement. This is incompatible with anonymous peer-reviewers accessing
the data, and the named [open peer review
policy](http://gigasciencejournal.com/blog/peerrevwk17-update-peer-review-week-goes-transparent-and-interactive/)
from *GigaScience* is therefore a necessity for such datasets. In
practice one reviewer requested the data access like other researchers
usually do [through the EGA
platform](https://www.ebi.ac.uk/ega/studies/EGAS00001003699) where our
data is hosted, the Data Access Committee granted him access and he was
able to review the data. 

<figure class="wp-block-image size-large is-resized">
<img
src="http://gigasciencejournal.com/blog/wp-content/uploads/2020/10/Screenshot-2020-10-22-at-10.33.20-AM-1024x659.png"
class="wp-image-3591" loading="lazy" decoding="async"
srcset="http://gigasciencejournal.com/blog/wp-content/uploads/2020/10/Screenshot-2020-10-22-at-10.33.20-AM-1024x659.png 1024w, http://gigasciencejournal.com/blog/wp-content/uploads/2020/10/Screenshot-2020-10-22-at-10.33.20-AM-300x193.png 300w, http://gigasciencejournal.com/blog/wp-content/uploads/2020/10/Screenshot-2020-10-22-at-10.33.20-AM-768x494.png 768w, http://gigasciencejournal.com/blog/wp-content/uploads/2020/10/Screenshot-2020-10-22-at-10.33.20-AM.png 1390w"
sizes="(max-width: 768px) 100vw, 768px" width="768" height="494" />
</figure>

**You also share data, source code, and compute environments via a
Nextjournal interactive notebook [supplement to the
paper](https://nextjournal.com/rarecancersgenomics/a-molecular-map-of-lung-neuroendocrine-neoplasms/),
so what advantages does this resource bring, and what do you hope users
will do with it?**

We wanted to allow anyone to be able to re-use [our
data](http://dx.doi.org/10.5524/100781), including researchers with
limited bioinformatics or biostatistics expertise or infrastructure.
Therefore we made a particular effort not to only provide raw sequencing
data and final results but also intermediate standardized re-processed
as well as reproducible, open-source, easy to use bioinformatics
pipelines and source code. We also provide an interactive map hosted on
the [UCSC TumorMap
portal](https://tumormap.ucsc.edu/?p=RCG_lungNENomics/LNEN) that can be
explored like any geographical map simply using a web browser. The
entire process to reproduce entire analyses is described in an
interactive computational notebook [hosted on
Nextjournal](https://nextjournal.com/rarecancersgenomics/a-molecular-map-of-lung-neuroendocrine-neoplasms/),
and one can easily update the molecular map by integrating new samples
starting from raw sequencing reads.

<figure class="wp-block-image size-large is-resized">
<img
src="http://gigasciencejournal.com/blog/wp-content/uploads/2020/10/Screenshot-2020-10-22-at-10.53.02-AM-1024x935.png"
class="wp-image-3592" loading="lazy" decoding="async"
srcset="http://gigasciencejournal.com/blog/wp-content/uploads/2020/10/Screenshot-2020-10-22-at-10.53.02-AM-1024x935.png 1024w, http://gigasciencejournal.com/blog/wp-content/uploads/2020/10/Screenshot-2020-10-22-at-10.53.02-AM-300x274.png 300w, http://gigasciencejournal.com/blog/wp-content/uploads/2020/10/Screenshot-2020-10-22-at-10.53.02-AM-768x702.png 768w, http://gigasciencejournal.com/blog/wp-content/uploads/2020/10/Screenshot-2020-10-22-at-10.53.02-AM-1536x1403.png 1536w, http://gigasciencejournal.com/blog/wp-content/uploads/2020/10/Screenshot-2020-10-22-at-10.53.02-AM.png 1756w"
sizes="(max-width: 768px) 100vw, 768px" width="768" height="701" />
<figcaption>Nextjournal supplement of the paper with code and
dynamically generated figures.</figcaption>
</figure>

Promoting such data integration will empower more reliable statistical
testing, and this map will therefore serve as a reference in future
studies, ultimately leading to a better understanding of this rare
understudied disease. In the medium term, as high throughput sequencing
technologies start to be used for personalized medicine, these molecular
maps could be used as a reference map to locate patients, helping for
the diagnosis and decision-making.

## References {#references .wp-block-heading}

Gabriel AAG, Mathian E, Mangiante L, Voegele C, Cahais V, Ghantous A,
McKay JD, Alcala N, Fernandez-Cuesta L, Foll M. A molecular map of lung
neuroendocrine neoplasms. Gigascience. 2020 Oct 30;9(11):giaa112. doi:
[10.1093/gigascience/giaa112](https://doi.org/10.1093/gigascience/giaa112).

Gabriel AAG; Mathian E; Mangiante L; Voegele C; Cahais V; Ghantous A;
McKay JD; Alcala N; Fernandez-Cuesta L; Foll M (2020): Supporting data
for "A molecular map of lung neuroendocrine neoplasms." GigaScience
Database. <http://dx.doi.org/10.5524/100781>

The post [Accessing and reviewing Controlled Access Data of Rare
Cancers. Q&A with Matthieu
Foll](http://gigasciencejournal.com/blog/controlled-access-data/){rel="nofollow"}
appeared first on
[GigaBlog](http://gigasciencejournal.com/blog){rel="nofollow"}.

A molecular map of lung neuroendocrine neoplasms

Supporting data for "A molecular map of lung neuroendocrine neoplasms."

We have a Q&amp;A with author Matthieu Foll from the WHO International Agency for Research on Cancer

(IARC)

 on his new GigaScience paper presenting multi-omic data from rare lung neuroendocrine neoplasms and his experience having the precious


 Controlled Access data peer reviewed by named peer reviewers.

As a journal focused on reproducibility of research

 GigaScience

has a strict open-science policy, meaning

Messaggi di Rogue Scholar

Opening access and exploring sepsis in an Intensive Care database. Q&A with Tom Edinburgh

Accessing and reviewing Controlled Access Data of Rare Cancers. Q&A with Matthieu Foll