(2024) 22:307
Savva et al. BMC Medicine
https://doi.org/10.1186/s12916-024-03460-6

BMC Medicine

RESEARCH ARTICLE

Open AccessAn original study assessing biomarker
success rate in breast cancer recurrence
biomarker research
K‑V. Savva1*†, A. MacKenzie2†, R. C. Coombes1, N. M. Zhifang2, B. G. Hanna1 and C. J. Peters1

Abstract
Background Breast cancer is the second most common cause of cancer mortality worldwide. Biomarker discovery
has led to advances in understanding molecular phenotyping and thus has a great potential for precision manage‑
ment of this diverse disease. Despite increased interest in the biomarker field, only a small number of breast cancer
biomarkers are known to be clinically useful. Therefore, it is very important to characterise the success rate of biomark‑ers in this field and study potential reasons for the deficit. We therefore aim to achieve quantitative characterisationof the biomarker translation gap by tracking the progress of prognostic biomarkers associated with breast cancer
recurrence.
Methods An electronic systematic search was conducted in Medline and Embase databases using keywords
and mesh headings associated with breast cancer recurrence biomarkers (1940–2023). Abstracts were screened,
and primary clinical studies involving breast cancer recurrence biomarkers were selected. Upon identification of rel‑evant literature, we extracted the biomarker name, date of publication and journal name. All analyses were performed
using IBM SPSS Statistics and GraphPad prism (La Jolla, California, USA).
Results A total of 19,195 articles were identified, from which 4597 articles reported breast cancer biomarkers associ‑ated with recurrence. Upon data extraction, 2437 individual biomarkers were identified. Out of these, 23 are currently
recommended for clinical use, which corresponds to only 0.94% of all discovered biomarkers.
Conclusions This study characterised for the first time the translational gap in the field of recurrence-related breast
cancer biomarkers, indicating that only 0.94% of identified biomarkers were recommended for clinical use. Thisdenotes an evident barrier in the biomarker research field and emphasises the need for a clearer route from bio‑
marker discovery through to implementation.
Keywords Breast cancer, Biomarkers, Translational research, Clinical utility†

K-V Savva and A. MacKenzie contributed equally to this work.

*Correspondence:
K‑V. Savva
k.savva17@imperial.ac.uk
1
Department of Surgery and Cancer, Imperial College London, 10th Floor
QEQM Wing, St Mary’s Hospital, London W2 1NY, UK
2
Division of Surgery, Department of Surgery and Cancer, Imperial College
London, London, UK

Background
According to GLOBOCAN 2018, breast cancer is thesecond leading cause of cancer death worldwide [1].
Breast cancer is highly heterogenous and encompasses
many distinct biological entities with specific biologicalfeatures and pathological behaviours [2]. Approximately
30% of patients experience recurrence while 90% of cancer-related deaths are due to metastatic recurrence [3–5].
A range of prognostic factors have been associated with

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Savva et al. BMC Medicine(2024) 22:307

the incidence of recurrence including age, tumour size,stage and lymph node involvement [6].
Discovery of biomarkers associated with cancer is animmensely researched field because of the associatedclinical utility they offer; yet discerning which biomarkers are clinically useful is challenging. Indeed, despite thehigh rate of biomarker discovery, only a small percentage
of biomarkers are directly impacting patient care, indicating a shortfall in the route toward translation. Thishas driven us in developing the Biomarker Toolkit, a tool
used to mediate the successful translation of biomarkers
from bench to bedside [7].
A more promising biomarker would usually attract
academic interest [8]. This would result in an increased
number of publications that are of higher quality [8].
Although factors associated with the biomarker translational gap have been discussed by numerous studies, aquantitative assessment of this gap is currently lacking.
To this end, this original study primarily aims to quantitatively characterise the scale of biomarker translation gap, in the field of recurrence-related breast cancer
biomarkers. This will be achieved by tracking the upto-date progress of all prognostic biomarkers directly/
indirectly associated with breast cancer recurrence. Aiming to examine specific factors associated with biomarker
success, we also sought to investigate the relationshipbetween biomarker clinical utilisation and publication
quality/quantity (i.e. publication frequency and article
impact factor), while suggesting potential solutions toimprove biomarker translation rate.

Methods
Systematic search

A systematic literature search with no date limit was
conducted in Ovid, using Medline and Embase databases, to obtain primary literature linking breast cancer biomarkers and prognosis/recurrence (search
conducted in June 2023). Primary clinical studies referto original research papers which conduct biomarker
evaluation using clinical studies. The main keywords
used included: “breast cancer”, “biomarker” and “recurrence”. The terms were expanded and adjusted to eachdatabase. Broad keywords were selected/utilised in the
search in order to avoid losing relevant publications.
The full search strategy including all keywords andterms used can be found in Additional file: Table S1.
Inclusion criteria included primary studies which considered: breast cancer, a molecular biomarker, reference
to recurrence, disease-free survival and/or metastasis. Exclusion criteria included conference abstracts,
reviews, editorials, case studies, commentaries, letters and studies not published in the English language.
A subset of the biomarkers included in this review are

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well known to have additional roles. For example, ER
and PR are routinely utilised to subtype breast cancer
patients. In this review, if any biomarker was associatedwith breast cancer recurrence-related outcomes, asdetailed in the Additional file: Table S1, it was selected
for inclusion. Duplicate citations were removed using
the referencing software Mendeley. The PRISMA
guidelines and flow chart were used to guide the literature search (PRISMA checklist and Fig. 1). Once eligible articles were identified, the date of publication,
journal and biomarker name were extracted from theabstracts and were tabulated using Microsoft Excel.
Biomarker candidates sharing identical characteristics
but referenced by diverse nomenclatures were amalgamated into unified entities. Distinct variations or versions of a biomarker candidate, including assessments
using different subtypes, were treated as discrete entities. Furthermore, panels comprising biomarker candidates were regarded as distinct entities, irrespective ofpotential duplication of previously identified biomarkers within them. Publication screening was conductedby two individual reviewers and any disagreements
between reviewers were discussed and resolved (KVS
and AM).
Biomarker publication frequency and survival analysis

All relevant articles were manually grouped by biomarker
using Microsoft Excel. The total number of biomarkers
identified and the total publication frequency for eachbiomarker were recorded. A frequency bar chart wasconstructed to identify the publication frequencies forall biomarkers. Biomarkers were grouped into 1, 2–5,
6–10, 11–20 and > 20 publications and the frequency ofbiomarkers in each category was plotted. Time difference(years) was calculated between the first publication andeach subsequent publication for each biomarker, to allow
a fair comparison between biomarkers published in different years.
Clinical use data

All identified biomarkers were separated into two groups:(i) successful and (ii) stalled biomarkers. Successful biomarkers were defined as biomarkers which have been
recommended/approved by any of the following guidelines/regulatory bodies: NICE guidelines [9–11], ASCO
Clinical Practice Guidelines [12], FDA [13], NCCN [14],European Group on Tumour Markers (EGTM) [15] andSt Gallen Consensus [16] (Table 1). Conversely, stalledbiomarkers are defined as biomarkers which are (i) notrecommended/approved by the previously mentioned

Savva et al. BMC Medicine(2024) 22:307

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Fig. 1 PRISMA illustrating the flow of literature selection for the systematic searchguidelines/regulatory authorities and (ii) are not clinically utilised.
Identifying any relation between journal impact factor
and biomarker clinical implementation

Journal impact factors for all publications were retrieved
from an online database [17] (https://​www.​scijo​urnal.​org/). The highest impact factor publication was selected
for each biomarker. A box plot was constructed to illustrate the maximum, median and minimum highest
journal impact factor, for both successful and stalled biomarker groups.
Evaluating differences between stalled and successful
biomarker prognostic outcomes using cBioPortal

In an attempt to compare the survival outcomes relatedwith specific biomarker-associated mutations, cBioPortal was utilised. Survival outcomes of all successful andstalled biomarkers associated with mutations with morethan 12 publications were assessed in cBioPortal (https://​www.​cbiop​ortal.​org/). We have considered all identified

biomarkers with > 12 publications, irrespective of biomarker nature. P-values comparing the survival outcome between the mutant and wildtype version of thebiomarker from 16 breast cancer studies (6805 breast
samples/6391 patients) were extracted, details are listed
in Additional File: Table S3. P-values were subsequently
compared between the stalled and successful biomarkers.
Statistical analysis

All data analysis was conducted using Graphpad Prism(La Jolla, CA, USA) and IBM SPSS Statistics version 26.0
(IBM Corp., Armonk, NY, USA). P value ≤ 0.05 was used
to denote significance. IBM SPSS Statistics was used torun a Kolmogorov–Smirnov test and assess data normality. A Mann–Whitney U test was applied to evaluate differences between the two groups for both average
publication frequency and journal impact factor. A random selection function was employed to procure ten distinct sets of 23 values each for (a) publication frequency
and (b) impact factor from the stalled biomarker group.
Random function was conducted to select ten batches ofstalled biomarkers, aligning their count with that of theSavva et al. BMC Medicine(2024) 22:307

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Table 1 Table indicating successful biomarkers as recommended by EGTM, ASCO, St. Gallen’s consensus and NCCN
EGTM

ASCO

St.Gallen

NICE

FDA

NCCN

ER

X

X

X

X

X

X

PR

X

X

X

X

X

X

Ki-67

X

HER2

X

X

X

X

X

OncoDX

X

X

X

X

MammaPrint

X

BCI

X

X

X

X

X

X
X

X

X
X

Prosigna

X

X

X

X

Endopredict

X

X

X

X

X

X
X

UPA and PAI-1

X

X

CEA

X

X

X

X

CA15-3

X

X

X

X

X

X

X

CA 27–29
Luminal A

X

Luminal B

X

TNBC

X

Basal
BRCA1/2

X
X

PIK3CA

X

X

TILs
P53

X

X
X

X
X

X

CTC​

X

PDL1

X

ER estrogen receptors, PR progesterone receptors, HER2 human epidermal growth factor receptor 2, OncoDx OncotypeDx, BCI Breast Cancer Index, UPA urokinase
plasminogen activator, PAI-1 plasminogen activator inhibitor type-1, CEA carcinoembryonic antigen, CA15-3 cancer antigen 15–3, CA 27–29 cancer antigen, TNBC
triple negative breast cancer, BRCA1/2 BReast CAncer gene 1/2, PIK3CA phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha, TILs tumour-infiltrating
lymphocytes, P53 tumour protein p53, CTC​ circulating tumour cells, PDL1 programmed death-ligand 1

successful group, thereby facilitating statistical analysis.
Binary logistic regression was conducted to assess thepossible correlation between biomarker success and (i)publication frequency, (ii) cBioPortal prognostic data,and (iii) publication impact factor. All graphs were produced in GraphPad Prism 7 (La Jolla, CA, USA).

Results
Upon identification and screening of 19,195 articles,
4597 studies were selected as they studied breast cancer biomarkers which (i) provided additional prognostic
information in terms of recurrence as compared to conventional biomarkers or (ii) could be used as predictive
for benefit from a particular therapy associated withrecurrence. A detailed process of literature selection is
illustrated in the PRISMA flow diagram shown in Fig. 1.
Upon data extraction, 2437 individual biomarkers wereidentified.
Out of the 2437 biomarkers identified, 23 are currently
recommended for clinical use, which accounts for only
0.94% of all discovered breast cancer recurrence biomarkers (Fig. 2A). Moreover, differences in impact factor

and publication frequency were investigated between
stalled and successful biomarkers. Average publishedliterature was significantly higher in successful biomarkers with a median of 79 published papers for successful and 1 publication for the stalled biomarker group(Fig. 2B). As indicated in Fig. 2C, there is an extremely
high frequency of biomarkers with a single publicationin the stalled biomarker group (77.34%). In contrast, thesuccessful biomarker group did not have any biomarkers with a single publication, while 91.7% biomarkers
had > 20 publications. The average highest impact factorof successful biomarkers, as indicated by Fig. 2D, was significantly higher in comparison to the stalled biomarker
group (p ≤ 0.0001). Interestingly, the maximum journal
impact factor recorded for the successful biomarkers
was 51.77, a biomarker published in the Annals of Oncology in 2021 (Ki-67) [18]. Although Fig. 2B and D display
all individual data points for the respective graphs, thestatistical analysis employed a random selection function to obtain ten distinct sets of 23 values each for (a)publication frequency and (b) impact factor from thestalled biomarker group. The random selection process

Savva et al. BMC Medicine(2024) 22:307

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Fig. 2 Success rate of breast cancer recurrence biomarkers. A Pie chart indicating % of clinically adopted breast cancer recurrence biomarkers. B
Box plot indicating the median number of published papers, taking into consideration all data points for successful (n = 23) and stalled (n = 2091)
biomarkers C Biomarkes frequency bar chart illustrating the number of biomarkers for publication ranges 1, 2–5, 6–10, 11–20 and > 20 (n = 2115).
D Box plot indicating the median highest impact factos. Taking into consideration all data points for each successful and stalled biomarkers.
The upper and lower limits of the plots represent max and min values. Successful biomarker (i) publication frequency and (ii) impact factor
cohorts (n = 23) were compared to ten independent randomly selected stalled biomarkers. In every separate comparison for both publication
frequency and impact factor, using each subset of randomly selected data, the Mann–Whitney U test yielded a P-value of < 0.0001. Asterisks shownin the graph indicate the level of significance where ns: P > 0.05 *: P ≤ 0.05, **: P ≤ 0.01, ***: P ≤ 0.001, **** P ≤ 0.0001

was conducted to choose ten batches of stalled biomarkers, ensuring their count matched that of the successful
group, thereby facilitating statistical analysis. In support,
publication frequency and journal impact factor weresignificantly associated with clinically successful biomarkers, using binary logistic regression (publication frequency: p = 0.002 (95% CI: (0.904–0.978)), impact factor:p ≤ 0.0001, 95% CI: (0.778–0.890)).
To compare the publication timeline between the twogroups, a biomarker survival analysis was conducted, inwhich random selection was used to select five biomarkers from each group. As indicated in Fig. 3, there is anevident difference in both publication number and publication window between the two groups. In specific,
successful biomarkers (Fig. 3A–E) appear to have more
published papers per year, over a longer period of timethan stalled ones (Fig. 3A–E vs F–J).

As seen in Fig. 3H, the first published paper regarding Gasdermin was in 2015. To account for the fact thatsome biomarkers are relatively new (recently published),
and therefore did not have time to accumulate interest;
the time in (years) between the biomarker’s first clinical study and adoption/recommendation date was used
to re-calculate the percentage of successful biomarkers.
Specifically, the median years of adoption between thefirst publication and the date of recommendation/adoption for all successful biomarkers was 6 years. Thus, allbiomarkers with a first clinical publication from 2017
onwards (2017 = 2023–6) were marked and excluded
from the % successful biomarker calculation. Upon
removal of these biomarkers from the total number ofidentified biomarkers, the percentage survival increased
from 0.94 to 1.42% (< 2%, successful BMs:23 vs stalled
BMs:2414).

Savva et al. BMC Medicine(2024) 22:307

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Fig. 3 Biomarker Survival Analysis. Bar charts indicating the number of published papers over the years for A–E Successful and F–J stalledbiomarkers. Random function of Excel was used to select five biomarkers for each group. Blue box squares represent 5- and 10-year windows
since first publication (first square = 0–5-year window, second square = 5–10-year window). BCI, Breast Cancer Index, HER2, human epidermal
growth factor receptor 2; SPG130, solouble glucoprotein

Savva et al. BMC Medicine(2024) 22:307

The influence of biomarker test commercialisation andregulatory authority approval on publication frequency
was also examined. Biomarkers presented in Fig. 4 wereselected as they were the only commercialised biomarkers. As seen in Fig. 4, test commercialisation, as well as
FDA & NICE approval, do not evidently result in alteredpublication frequency.
cBioPortal was utilised to assess prognosis-related outcomes in all successful and stalled biomarkers with morethan 12 publications (Additional file: Fig. S1). As indicated in Additional file: Fig. S2, P-values for disease-freesurvival, disease-specific survival, progression-free survival, relapse-free survival and overall survival appear tohave similar distributions between stalled and successful
biomarkers. P-values extracted assess the extent of difference in outcomes between the mutated and wildtype
version of the biomarker, in each cohort. Binary logistic
regression illustrated no significant association between
reported P values for each prognostic outcome and biomarker clinical implementation status (see Additional
file: Fig. S2 and Table S4).

Discussion
The gap between biomarker discovery and clinical translation is large. The aim of this novel study is to quantitively characterise this gap using recurrence-related
breast cancer biomarkers. Key outcomes of this study
include (i) a high rate of discrepancy between biomarker
discovery and clinical translation as only 0.94% of identified biomarkers are currently recommended for clinicaluse by guidelines/regulatory authorities and (ii) a significantly higher frequency of publications, published over alarger time-frame and a higher impact factor for successful biomarker publications. Other than assessing the relation of these factors with biomarker success, this study
reinforces the need to identify additional features thatwould enable researchers to assess a biomarker’s potential for clinical implementation.
The recurrence breast cancer biomarker translation
rate was critically low, 0.94%. Consistently low clinicalimplementation rates in the literature support our findings and emphasise the lack of efficiency within the biomarker pipeline [19, 20]. Potential barriers of biomarker
implementation are well documented in the literature.

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Numerous impediments inhibiting the discovery anddevelopment of biomarkers can be attributed to incomplete, inappropriate, or inadequately reported scientificfindings. These barriers encompass a complex array offactors, including insufficient sample size, inappropriate
statistical methodologies, absence of clinical relevance,
inadequate characterisation of study cohorts, limited
analytical validation, biological variability, and potential
biases stemming from sponsor influence, among others[21, 22]. Although numerous studies highlight the translation barriers and the low number of clinically used biomarkers, only a few of them quantitively assess this [23,24], emphasising the importance of the current study.
Assuming that successful biomarkers would have alarger evidence base due to greater scientific interest,
the relationship of biomarker success and publication
frequency was explored. In support, 91.67% of the successful biomarkers were in the > 20 publication category(Fig. 2C). This is in stark contrast to the stalled biomarker
group in which only 0.29% were in the > 20 publication
category. Moreover, 77.34% of stalled biomarkers had asingle publication. This reinforces the concept that there
is a high rate of biomarker discovery taking place in academia, but very little progression of these biomarkers
towards clinical utilisation, further supporting the literature as well as the study hypothesis [25].
As expected, successful biomarkers were significantly
associated with higher publication number, using binary
logistic regression (publication frequency: p = 0.002 (95%
CI: (0.904–0.978)), with a more consistent increase inpublications over time, as indicated in Fig. 3. The low
frequency of published literature in the stalled biomarker group could be due to a lack of biomarker
efficacy in predicting recurrence or lack of standardised
biomarker assay employment. However, drawing conclusions on this seems unfounded as we have not analysed
the strength of the relationship between biomarkers andrecurrence, while the cBioPortal analysis performed
seems to suggest no differences between the groups(Additional file: Fig. S2).
Low publication frequency in certain cases might bebecause some biomarkers are newer than others; therefore, the date of publication should also be considered.
Considering the influence of this factor, publication(See figure on next page.)
Fig. 4 Commercialisation, authority approval and biomarker success. Bar charts indicating the number of published papers over the years for A
Oncotype Dx, B MammaPrint, C BCI, D Endopredict and E PAM50. These biomarkers were the only biomarkers identified to be commercialised. Allof the stated biomarkers are recommended by guidelines/regulatory authorities. BCI is not currently approved by FDA/NICE, but is recommendedby NCCN, ASCO and EGTM. The period of biomarker test commercialisation is indicated by the light blue window, the NICE approval dateis denoted by the broken line while the FDA date of approval is illustrated by a continuous line. PAM50 (also referred as Prosigna): Prediction Analysis
of Microarray 50, BCI, Breast Cancer Index

Savva et al. BMC Medicine(2024) 22:307

Fig. 4 (See legend on previous page.)

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Savva et al. BMC Medicine(2024) 22:307

frequency within a 5- and 10-year window was evaluated. Within the 5-year window, at least two papers were
published in the successful biomarker group, while mostof the stalled biomarkers had a single publication. This
supports our finding that Fascin and Synuclein might
be promising biomarkers, as they have a larger numberof biomarker publications (Fig. 3G and J). Almost all of
the successful biomarkers had > 6 publications (Fig. 3), in
contrast the stalled group which at most cases (SPG130,(TA)9 repeats and Gasdermin) published only a single
paper within 10 years. Nevertheless, the 10-year window
comparison was not possible for all of the biomarkers,
since some of them were published less than 10 years ago.
For example, Gasdermin (Fig. 3H) will naturally have alower number of publications as this biomarker has not
yet had time to stimulate interest in the biomedical community. To account for this confounder, all biomarkers
with a first clinical publication from 2017 onwards were
excluded from the % successful biomarker calculation, as
described in the result section. This resulted in a slight
improvement in the % biomarker translation rate, from
0.94 to 1.42%, which is still extremely low. Moreover, 3(0.43%) of the biomarkers discovered from ≥ 2017 had
five publications, supporting that these biomarkers could
be promising, as they have increased interest.
The FDA biomarker Qualification Framework stresses
the importance of a strong relationship between biomarker and outcome, sample size and reproducibility of results [26]. Naturally, a thorough assessment ofthese analytical validity parameters requires numerous studies. This may explain the positive relationshipbetween publication frequency and success [27]. Thisfinding can in part be because biomarkers featured in
guidelines would raise awareness and interest in a biomarker. A solid evidence base would also need to be
initially present for the biomarker to be approved. Thispattern is supported by Fig. 3 where both Oncotype Dx
and Endopredict accumulated numerous publication
evidence prior to approval. However, as indicated inFig. 4B some biomarkers may reach success after a relatively low number of publications. This demonstrates
that biomarker success cannot always be predicted bypublication frequency and distribution in isolation.
Assessing the frequency of biomarker publication andcorrelating this to biomarker success was an initialattempt to detect factors associated with successful
biomarker implementation. As supported by cBioPortal data, there was no significant difference between
the clinical performance of stalled and successful biomarkers (Additional file: Fig. S2). This supports thefinding that the performance of a biomarker reported
in literature does not clearly indicate how promising abiomarker will be for clinical use. This reinforces the

Page 9 of 12role of the Biomarker Toolkit, a novel tool that provides quantitative biomarker evaluation by considering the accuracy of reporting in four pillars including
rationale, analytical validity, clinical validity, and clinical utility [7]. The output generated by this tool has
the potential to predict biomarker success and direct
future investigations towards evaluating supplementary
attributes, such as the nature of the study (e.g., preclinical/in vitro studies, clinical trials, high-throughput prospective or retrospective biomarker discovery studies).
This expanded analysis could enhance the predictive
capacity for biomarker success and consequently direct
research interest.
Biomarker commercialisation is a vital step in the biomarker development; hence we also investigated the relation of commercialisation with publication frequency.
Interestingly, Mammaprint showed a rapid rise of published literature upon FDA approval. Nevertheless, thispattern might be due to the fact that MammaPrint wascommercialised very early. Commercialisation fortifies
test standardisation and provides a promising factor that
could be linked with biomarker clinical implementation. Hence, it would be logical to assume that biomarker
commercialisation would attract additional interest[28]. However, current data indicated that publication
density does not appear to change dramatically uponguideline recommendation or commercialisation. Thismight be due to a lack of accurate reporting or insufficient published data prior to test commercialisation toallow assessment of publication frequency. Interestingly,
IHC4, demonstrated a similar pattern to MammaPrint
in terms of publications (Additional File: Fig. S1), however, although assessed by guidelines, it is not currently
recommended mainly due to the limited reproducibility/
analytical validation of the immunohistochemical technique used to assess this biomarker (NICE guidelines).
This reinforces the idea that methodological and analytical validation aspects should be addressed at an earlystate to enhance biomarker clinical translation. Overall, commercialisation and authority approval do notappear to have an evident association with publication
frequency.
Due to the large numbers of stalled biomarkers withminimal publications, the Excel random function
selected stalled biomarkers with lower publication frequencies (Fig. 3). However, there is a minor subset of biomarkers which have over 20 publications (see Additional
File: Table S2). Although these biomarkers are not currently clinically used, the high level of research papers
suggests that scientists, clinicians, and industry should
study the evidence of these biomarkers closely and assess
the reasons which have prevented them from being clinically adopted. As emphasised by many authors potential

Savva et al. BMC Medicine(2024) 22:307

biomarkers might lack a robust analytical methodology
or evaluation studies, including cost-effectiveness andfeasibility studies. Therefore, this study reinforces the factthat biomarker progress should be studied in detail to
identify areas of improvement that could promote theiradoption in the clinic, emphasising the importance of the
Biomarker Toolkit [7].
In support of the hypothesis, the journal impact factor was significantly higher for successful biomarkers incomparison to stalled (p < 0.0001). The highest impact
factor for each biomarker was used to assess the biomarkers at peak levels of research quality and interest,
where publications have been subjected to rigorous peer
review. The highest impact factor publication noted was
51.77, a biomarker published in the Annals of Oncology
in 2021 (Ki-67) [18]. This publication fulfilled many of thecriteria suggested by the FDA for biomarker translation
success including large sample size, defined cohort, clinical intent, and powered statistics indicating that, in thiscase, high impact factor reflects high-quality research
publication [29]. However, whilst the journal impact factor is useful as an overview of publication quality andacademic success, it should be interpreted with caution
as the score is subject to artificial inflation by practice ofself-citation [30, 31]. Therefore, whilst it is useful to geta broad overview of interest a biomarker is attracting, it
is not necessarily the optimal measure to assess research
quality [32].
Whilst this study identifies candidate biomarkers andqualities that are likely associated with their successful clinical utility, it does not discriminate between thestrength of evidence/ association with recurrence ofeach biomarker. Future research may also utilise the Biomarker Toolkit to score biomarkers. This will eventually assist in prioritising research into those biomarkers
with increased chances of clinical utilisation. These data
could be integrated within a centralised database whichmight prioritise future research, keeping scientists andclinicians up to date on biomarker discovery [33]. Comprehending the factors influencing the clinical utility ofbiomarkers is poised to steer research focus, mitigate
redundant exploration and its associated costs, enhance
the rate of biomarker translation, and, most importantly,
contribute significantly to improving patient outcomes[33].
Nevertheless, this novel study also suffers from some
limitations. Specifically, abstracts alone were screened
and articles were rejected if insufficient information waspresent. This may be inappropriate since many journals
have word-limited abstract content and therefore crucial information necessary to fulfil the inclusion criteria may have been presented in the full-text only. Dueto the extremely large number of publications, full-text

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screening would be unrealistic considering the timescale of the study. Furthermore, this study focuses onrecurrence-related breast cancer biomarkers; future
work should aim to assess the transitional rate of additional biomarker types for other cancer types. Focus was
directed on recurrence rather than overall patient prognosis, to maintain a realistic number of Ovid results.
This study defined successful biomarkers, as those
approved/recommended by regulatory bodies and guidelines. While exploring previously mentioned guidelines(see Table 1), we identified that different sets of biomarkers were recommended in some cases, and in many
instances, it was difficult to trace up-to-date publishedrecommendations. In certain cases, there was no clear
repository of recommended biomarkers associated withrecurrence. This emphasises the need for better reporting and co-ordination between the different bodies todevelop a user-friendly repository with biomarker recommendations, for different cancer and biomarker types.

Conclusions
In summary, this study is the first attempt at (i) assessing
the percentage of clinically useful breast cancer biomarkers directly/indirectly associated with recurrence and(ii) in evaluating the relationship between impact factor
and publication frequency with biomarker clinical implementation. Future work will aim to examine in detailbiomarker publications to define literature-reported
characteristics associated with a clinically useful biomarker. This will allow biomarker assessment and enable
researchers to (i) critically evaluate biomarker researchand (ii) identify biomarkers that are more likely to beclinically useful. Thus, research interest and funding
could then be directed in a targeted manner, minimising
the translational gap and ultimately improving patientcare.
Abbreviations
BCI	Breast Cancer Index
BRCA1/2	BReast CAncer gene 1/2
CA 27-29	Cancer antigen 27–29
CA15-3	Cancer antigen 15–3
CEA	Carcinoembryonic antigen
CTC​	Circulating tumour cells
EGTM	European Group on Tumour Markers
ER	Estrogen receptors
FDA	Food and Drug Administration
HER2	Human epidermal growth factor receptor 2
NCCN	National Comprehensive Cancer Network
OncoDx	OncotypeDx
P53	Tumour protein p53
PAI-1	Plasminogen activator inhibitor type-1
PAM50 (also referred as Prosigna)	Prediction Analysis of Microarray 50
PDL1	Programmed death-ligand 1
PIK3CA	Phosphatidylinositol-4,5-bisphosphate
3-kinase catalytic subunit alphaPR	Progesterone receptors
SPG130	Soluble glycoprotein

Savva et al. BMC Medicine(2024) 22:307

TILs	Tumour-infiltrating lymphocytes
TNBC	Triple negative breast cancer
UPA	Urokinase plasminogen activator

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s12916-​024-​03460-6.
Supplementary Material 1: Supplementary Fig. 1. Biomarker Survival
Analysis: Bar charts indicating the number of published papers over theyears for IHC4. Supplementary Fig. 2. Evaluating differences between
stalled and successful biomarker prognostic outcomes using cBioPortal:
Survival outcomes of all successful and stalled biomarkers with more than
12 publications were assessed. P-values comparing the survival outcome
between the mutant and wildtype version of the biomarker from 16
breast cancer studies (6805 breast samples/6391 patients) were extracted.
Supplementary Table 1. Search terms used to assess biomarker successrate. Supplementary Table 2. Stalled BMs with greater than 20 publica‑
tions. Supplementary Table 3. cBioPortal Studies included in the analysis.
Supplementary Table 4. Binary logistic Regression Results assessing therelationship between biomarker success and prognostic outcomes.

Acknowledgements
We would like to show our gratitude to HealthTech Research Centre In Vitro
Diagnostics for supporting this project. We would also like to express our
gratitude to Dr. Alice E. Baggaley for providing feedback on the manuscript.
Infrastructure support for this research was provided by the NIHR Imperial
Biomedical Research Centre (BRC).
Authors’ contributions
KVS contributed to study design, article selection, data extraction, data
analysis, and manuscript write-up. AM contributed equally to article selection,
data extraction and data analysis. CRC, GBH, MNZ and CJP supported all stepsof the process, assisted in methodology/study design and also reviewed themanuscript. All authors read and approved the final manuscript.
Authors’ Twitter handles
Twitter handles: @profgeogehanna (Prof. George B. Hanna), @melodyni2018(Dr. Melody Ni Zhifang), @Oesophagusuk (Mr. Christopher J. Peters).
Funding
This study was funded by Imperial College London. Infrastructure support
was provided by the NIHR Imperial Biomedical Research Centre and the
NIHR Imperial Clinical Research Facility. The views expressed are those of theauthor(s) and not necessarily those of the NHS, the NIHR or the Department of
Health and Social Care.
Availability of data and materials
All data presented in the study can be found in the main and Additional file
document. Additional raw Data are available upon request.

Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Received: 9 January 2024 Accepted: 30 May 2024

Page 11 of 12

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