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Figure 1 Summary of international guidelines for pancreatic cysts surveillance and indication for EUS-FNA. IPMN: Intraductal papillary mucinous
neoplasm; MCN: Mucinous cystic neoplasm; EUS-FNA: Endoscopic Ultrasound with Fine Needle Aspiration; CT: Computed Tomography; MRI: Magnetic
Resonance Imaging; HGD: High Grade Dysplasia (European evidence-based guidelines on pancreatic cystic neoplasms 2018; Elta et al. 2018; Megibow
et al. 2017; Tanaka et al. 2017; Vege et al. 2015).
estimating the probability of PDAC in individuals with NOD,
undertaking validation studies of candidate biomarkers to
detect PDAC in the NOD group and providing a platform facilitating screening protocols PDAC in individuals with NOD
(Maitra et al. 2018). One such study, Early Detection Initiative
(EDI) is a randomized controlled trial designed to improve
detection of operable PDAC (Chari et al. 2022). The study uses
the Enriching New-onset Diabetes for Pancreatic Cancer
(ENDPAC) algorithm (Sharma et al. 2018), which stratifies subjects with NOD on the basis of age, and changes in both weight
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and diabetes parameters. Individuals with high ENDPAC scores
are stratified to an intervention arm, which includes imaging.
Comparisons will be made of the proportion of late stage PDAC
in the intervention and observation study arms.
A Cancer Research UK-funded study, UK Early Detection
Initiative (UK-EDI) is currently open in the UK and recruiting
people over 50 years of age who are within six months of their
diagnosis of diabetes in the UK (Pereira et al. 2020). With recruitment occurring from primary care, specialist diabetes centers and
secondary care, this study aims to create a unique resource in the
UK, including clinical data blood samples that will be used to
inform how best to screen for PDAC in NOD subjects in the UK,
including generation of data on the cost-effectiveness of diagnosing PDAC earlier in the setting of NOD. It is envisaged that the
UK-EDI resource will support the validation of candidate early
PDAC detection biomarkers as well as new biomarker discovery.
Biomarkers
Biomarkers for Detection
A number of approaches to develop biomarkers that could
facilitate screening for pancreatic cancer are being investigated.
Multi-cancer liquid biopsy tests, such as CancerSEEK and
GalleriTM have been tested for their sensitivity and specificity
for PDAC. CancerSEEK tests for mutations in cell free DNA
and eight cancer-associated blood protein biomarkers and was
observed to discriminate diagnosed PDAC from healthy control with a sensitivity and specificity of >70% and >99%, respectively (Cohen et al. 2018). The GalleriTM test, developed by
GRAIL, uses methylation signals from targeted circulating free
DNA and machine learning classifiers for cancer detection. In
a case-controlled observational study, involving subjects
already diagnosed with cancer or under investigation for highly
suspected cancer, the test detected 61.9% (13/21) of stage I,
60% (12/20) of stage II, 85.7% (18/21) of stage III, and 95.9%
(70/73) of stage IV pancreatic cancers (Klein et al. 2021).
In terms of screening for PDAC in the high-risk group of NOD,
the low prevalence of PDAC at approximately 1% in this group
represents a significant barrier, necessitating the further enrichment of this group for PDAC. Biomarker-based stratification of
individuals with NOD into high or low risk for PDAC, based on
whether diabetes is secondary to pancreatic disease (type 3c
diabetes mellitus, T3cDM), or the more prevalent T2DM is being
explored as a means of enriching for PDAC-related DM. The precise prevalence of T3cDM amongst individuals with DM is not
known, although various estimates, depending on study design
have been reported (Ewald et al. 2012; Vujasinovic et al. 2013;
Woodmansey et al. 2017). Briefly, in a study of 1,868 patients diagnosed with DM who were admitted to hospital in Germany, reclassification of their DM according to the American Diabetes
Association identified 172 patients (9.2%) that could be classified
as having T3cDM (Ewald et al. 2012). Of 150 patients with DM in
a single center in Slovenia, the prevalence of exocrine pancreatic
insufficiency was found to be 5.4% (Vujasinovic et al. 2013). In a
third study, when a cohort of 31,789 new diagnoses of adult onset
diabetes in primary care in the United Kingdom was examined for
pre-existing pancreatic disease, 559 individuals (1.75%) had a
diagnosed pancreatic disease which preceded their diagnosis of
diabetes (Woodmansey et al. 2017). This study differs from the
previous two in that no attempt at reclassification of diabetes status
of the whole cohort was made. We recently reported that the
combination of two blood-based proteins, adiponectin and interleukin-1 receptor antagonist (IL-1Ra) showed strong diagnostic
potential (AUC of 0.91; 95% CI: 0.84–0.99), for the distinction of
T3cDM from T2DM (Oldfield et al. 2022). More work will be
required using pre-diagnostic NOD cohorts to determine whether
biomarker-based stratification of this high-risk group, based on
the subtype of diabetes, will facilitate PDAC detection earlier.
There are numerous additional new approaches being taken
to detect PDAC early. Here we list a small selection of recent
advances. Ferguson et al. (2022) explored whether the diagnostic accuracy of single PDAC tumor cell-derived extracellular vesicles (EVs) could be improved by detecting mutated
cancer proteins in them. Having optimized single-EV analysis
(sEVA) using model lines, the authors were able to detect vesicles in 15 of 16 stage I pancreatic cancer patients. This work
provides an important proof-of-concept that sEVA may become
a useful method for earlier detection of pancreatic cancer.
The evidence that microbiota contribute to gastrointestinal cancer initiation, disease progression and treatment response is persuasive (Johnston and Bullman 2022). This has led researchers to
investigate the potential for alterations in an individual’s microbiome to facilitate PDAC diagnosis (Kohi et al. 2022). Using secretin-stimulated duodenal fluid, Kohi et al. examined bacterial and
fungal profiles from patients undergoing duodenal endoscopy
(Kohi et al. 2022). In a total of 308 patients, including 74 with
PDAC, 98 with pancreatic cysts, and 134 with normal pancreata,
the group reported that patients with PDAC had higher levels of
microbial DNA but lower microbial diversity compared to control
subjects. While encouraging, additional work is required,
including using pre-diagnostic samples, to address whether
duodenal microbiome profiles could aid stratification of PDAC
risk in individuals under surveillance for PDAC.
The potential for gut and oral microbiota to identify PDAC
have recently been investigated (Kartal et al. 2022; Nagata et al.
2022). Undertaking shotgun metagenomics and 16S rRNA
amplicon sequencing of fecal and saliva samples from Spanish
PDAC patients and controls, Kartal et al. identified a fecal classifier comprising 27 microbial species that showed accuracy for
discriminating PDAC from controls. This and other classifiers
performed equally well in a German validation cohort (Kartal
et al. 2022). Similarly, Nagata et al. trained fecal and saliva
metagenomics data from a Japanese cohort to discriminate
PDAC patients from control individuals, deriving signatures
including 30 gut and 18 oral species, associated with PDAC
(Nagata et al. 2022). In validation studies, based on the Spanish
and German cohorts mentioned above (Kartal et al. 2022),
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these signatures performed well in distinguishing PDAC. Of
note, in all three cohorts Nagata et al. reported significant
enrichments in the PDAC gut signatures of Streptococcus and
Veillonella spp and a depletion of Faecalibacterium prausnitzii
(Nagata et al. 2022). Taken together, these studies provide
promise that fecal and salivary microbiota may contribute to
early detection protocols for PDAC.
Advances in artificial intelligence provide automated tools
for capturing unique features in medical images that may not
be perceptible to the human eye. Qureshi et al. undertook
radiomic analysis of abdominal computed tomography (CT)
scans taken prior to PDAC diagnosis, identifying features predictive of PDAC. This led to a classifier, trained to perform
automatic classification of CT scans into healthy control or prediagnostic. The average classification accuracy on an external
dataset was 86% (Qureshi et al. 2022).
Predictive and Prognostic Biomarkers
Management of patients with pancreatic cancer is facilitated
by any factor that can be used to predict outcome prior to
treatment. This has been and will continue to be based on a
clinical evaluation of the patient and their tumor. The
assumption that patients with a larger more invasive cancer
will survive for less time than a patient with a smaller localized cancer is supported by a mass of empirical evidence,
nevertheless determining prognosis is considerably more
complex than determining stage, also accurately determining
stage in a patient prior to surgical intervention is not always
an easy task. The PET-PANC study confirmed the benefit of
conventional FDG-PET in staging cancers (Ghaneh et al.
2016), but although this is unquestionably a biomarker study,
we will avoid discussion of PET and other forms of imaging
(as these will be dealt with elsewhere). Similarly, grade of the
tumor is a determinant of prognosis and histopathological
assessment of grade is critical in giving an evaluation of
prognosis in adjuvant therapy and increasingly following targeted biopsy in patients with advanced disease, but again this
is a topic for another chapter (Chapter 20). Here we will only
consider surrogate markers of grade, stage, and the level of
residual disease subsequent to surgery or first line therapy
(Figure 2).
Prognostic Biomarkers
Prognostic Serum Markers
Serum levels of particular proteins or antigens (such as CEA,
PSA, alpha-fetoprotein, CA125) have long aided the diagnosis
of cancers. For PDAC, the most widely used marker is CA19-9.
CA19-9 levels are predictive of disease stage, resectability, progression, recurrence, and survival (Hartwig et al. 2011; Ko et al.
2005). Perioperative levels correlate with disease stage, and
independent predictor of survival in resectable cases (Ferrone
et al. 2006; Humphris et al. 2012). CA19-9 kinetics are a strong
predictor of response to treatments (Luo et al. 2021).
Postoperatively, CA19-9 levels <90U/ml can both predict
favorable response to adjuvant chemotherapy, as well as guide
the choice between FOLFIRINOX or gemcitabine (Luo et al.
2021). Evidence emerging from a large cancer database study in
28,000 patients (The National Cancer Database) showed stage
dependent decrease in survival, and points to benefit from neoadjuvant chemotherapy in patients with pre-operative CA19-9
levels >37 U/ml (Bergquist et al. 2016). CA19-9 response in
borderline resectable or locally advanced PDAC patients
receiving neoadjuvant therapy, can guide chemotherapeutic
switch and its duration improving post-operative disease outcomes and patient survival (Truty et al. 2021). Furthermore, as
demonstrated in a key study (MPACT trial) comparing
systemic therapies in metastatic PDAC, CA19-9 decline eight
weeks into treatment indicates a chemotherapeutic efficacy,
which can help in evaluating prognosis or even in suggesting a
need for a change in therapy (Chiorean et al. 2016).
Non-cancer Prognostic Markers
Prognosis is determined by the nature of the cancer, but also by
the patient who developed the cancer. Performance status measures including the Eastern Cooperative Oncology Group
(ECOG) and Karnofsky (KPS) are undoubtedly biomarkers,
and are central factors in prognostication, choice of treatment,
and care and patient inclusion in clinical trials, but here only
surrogate markers of performance will be considered. These
include markers of sarcopenia, gut dysbiosis, aging, and immunological response. Imaging techniques such as Dual Energy
X-ray Absorptiometry (DEXA) can be used to measure changes
longitudinally and relate them to cachexia and so to prognosis
(Cole et al. 2020). Similarly measures of immune response and
inflammation such a CRP (Laird et al. 2016) or Neutrophil
Lymphocyte Ratio (NLR) (Barker et al. 2020) are also related to
cachexia and hence prognosis. The immunological capacity of
a patient is both influenced and influences the gut microbiome,
making dysbiosis in this compartment an attractive biomarker
(Guo et al. 2022). Age is a well-known prognostic factor in
pancreatic cancer, although this might be due to different
management approaches in elder patients (van Dongen et al.
2022) but aside from chronological age, it also appears that
markers of biological age such as GDF15 relate not just to the
risk of cancer but also to its prognosis (Basisty et al. 2020).
Molecular Predictors of Prognosis
Cancer is fundamentally a genetic disease determined by the
genome of the cancer cells and their interaction with the soma
of the patient, impacting on the expression patterns of the
whole tumor (cancer and stromal cells). Tissue transcriptomic
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Figure 2 A summary of evolving biomarkers for screening/surveillance, prognostication, and prediction of treatment response in pancreatic cancer.
CA19-9 is the only clinically approved biomarker used for confirmation of diagnosis and prediction of stage, resectability, and monitoring of disease
progression.
studies have led to an evolving classification of pancreatic cancers (Bailey et al. 2016). Puleo et al. carried out RNA-sequencing
of formalin fixed paraffin embedded tissue. The authors
described 5 PDAC subtypes by integrating genomic data from
the neoplastic, stromal and immune compartments (Puleo
et al. 2018); Pure basal like (higher in mutations of CDKN2A
lymphocytes, CTLA4 expression and low proteasome/apoptotic signals) (Puleo et al. 2018). Survival was associated with
subtype; basal like (median OS 10.3 months), stroma activated
(median OS 20.2 months), desmoplastic (median OS 24.3
months), immune classical (median OS 37.4 months) and pure
classical (median OS 43.1 months).
and TP53, with KRAS Gly12Asp, Gly12Val mutations and with
greater metastatic spread along with scarce stroma), stroma
activated (higher MET and nuclear GLI1 expression suggesting increased hedgehog signaling; also increased α-SMA,
SPARC and FAP), desmoplastic (higher levels of CTLA4,
increased number of T-cells, more inflammatory and vascular
marker with lower tumor cell content), pure classical (characterized by KRAS Gly12Arg mutation, with high levels of human
equilibrate transporter 1; hENT1 and highly stromal), and
immune classical (marked stroma, NK-cells, T and B
Predictive Biomarkers
Contrasting Prognostic and Predictive Biomarkers
Therapeutic benefit is shown when individual patients survive
longer with treatment than without it. It is therefore easier to
demonstrate benefit in a patient with a very poor prognosis
without treatment than in a patient who would have a good
prognosis regardless of treatment. Therefore, far from being
equivalent to prognostic biomarkers, it is easier to show that a
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marker is predictive if it is inversely prognostic in the absence
of the specific treatment. It is therefore surprising that hENT1
(Human Equilibrative Nucleoside Transporter 1) expression
has been shown to be more highly expressed in the pure
classical subtype described above, as high expression of this
protein was shown to be associated with better survival in
patients treated with gemcitabine than patients treated with
5FU (at least partly due to poor prognosis in patient with high
expression of this protein when given alternative treatments)
(Aughton et al. 2021).
Predicting Predictive Biomarkers
Empirical identification of biomarkers that predict benefit of
one therapy over another is difficult, so in most cases specific
candidates are examined based on their theoretical relationship
to the treatments being investigated. For example, high levels of
a protein that is known to activate a particular pro-drug would
be expected to benefit the use of that pro-drug, while high
levels of the target of a particular drug would be expected to
make that drug less effective (simply on the basis that more of
the drug would have to reach the cancer cell to allow inhibition
of the target).
Choice of Chemotherapy
Targets and Drug Metabolism
As mentioned above the tumor cell levels of the nucleoside
transporter hENT1, which transports gemcitabine into
pancreatic cancer cells has been shown to benefit the use of
gemcitabine over a nucleobase therapy (5-FU) (Greenhalf et al.
2014). The rationale for testing this specific hypothesis is clear
(although, the fact that hENT1 also transports gemcitabine out
of cancer cells is often forgotten). Similarly, the rationale for
examining the effect of expression of the target of gemcitabine
(ribonucleotide reductase) is clear, but in this case the hypothesis that high levels would mean gemcitabine was less effective had to be rejected (at least in cells with low levels of hENT1)
(Elander et al. 2018). Another clear hypothesis was that the
protein cytidine deaminase (CDA), which metabolizes and so
potentially eliminates gemcitabine, should be associated with a
low level of response to that drug. Again, this was not seen
when the levels of the protein were examined, although when
the levels of the transcripts of CDA and hENT1 were examined
these were found not to relate to the levels of their respective
proteins and in the case of CDA, where CDA transcript levels
were high, the benefit of high hENT1 protein was found to be
lost, put another way, the benefits of hENT1 on gemcitabine
response were only seen in patients with low levels of CDA
transcript even though the low transcript levels were not
reflected in low levels of the protein (Aughton et al. 2021).
The majority of patients who have unresectable and meta-
static PDAC disease, which accounts for ~50% of patients with
PDAC, are often placed on a Gemcitabine-based chemotherapy,
like Gemcitabine and nab-paclitaxel (Abraxane®). FOLFIRINOX
is also a commonly utilized form of chemotherapy, used to treat
good performance status patients with PDAC (as discussed elsewhere), certainly in patients with good performance status
(Conroy et al. 2018). To be of use, a predictive biomarker applied
to most patients would therefore have to indicate that an
alternative treatment would be of more benefit (or of less harm)
than FOLFIRINOX. Applying the same rationale as above for
gemcitabine, based on the known action of the drug, is complicated by the fact that FOLFIRINOX is a combination therapy:
5-FU, Irinotecan and Oxaliplatin, all having different modes of
action. With respect to irinotecan, high expression of the prodrug metabolic enzyme carboxylesterase-2 (CES2) would obviously give greater sensitivity to the prodrug, and certainly there
is an association between sensitivity to irinotecan and level of
expression of CES2 in cell lines, with knockdown of CES2 having the predictable effect of increasing resistance to irinotecan
(Capello et al. 2015).
However, it is far from evidence that a tumor with high levels
of CES2 would be more sensitive to irinotecan, let alone that
this would mean that CES2 levels would predict response to the
combination of drugs in FOLFIRINOX, particularly in the context of adjuvant therapy where its expression levels would relate
to cells already removed from the patient. Control of CES2
expression is at least in part dependent on the level of expression of hepatocyte nuclear factor 4 alpha (HNF4A), which in
turn is upregulated in patients with diabetes mellitus (DM).
Thus, PDAC patients with DM have higher levels of CES2, so it
is reasonable to assume cancers would have greater sensitivity
to irinotecan. A mechanism maintaining this even once the
cancer cells are excised from the patient is supported by greater
irinotecan sensitivity of patient derived xenografts with higher
CES2 levels. This does not mean that patients with DM respond
better to FOLFIRINOX or even that patients with higher levels
of CES2 respond better, but it is supportive of that hypothesis.
To date CES2 association with prognosis in patients has been
supported (albeit in relatively small numbers of patients) but
proof that this is dependent on the choice of FOLFIRINOX as a
treatment option has yet to be provided (Capello et al. 2020).
For oxaliplatin, no prodrug activation is required and theoretically the key determinant of sensitivity (as with all platinum
based agents) is capacity to carry out nucleotide excision repair.
Sadly, when levels of enzymes involved in this pathway (e.g.
ERCC1) have been investigated, no predictive value has been
established in patients with pancreatic cancer (Tezuka et al.
2018).
Similarly, for Gemcitabine with nab-paclitaxel (Abraxane®)
as an alternative to FOLFIRINOX, the hoped for (and theoretically plausible) association of response with accumulation of
albumin-bound drugs, linked to the expression level of Secreted
Protein Acid and Rich in Cysteine (SPARC), was investigated
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and shown not to be adequate for use as a biomarker (Ormanns
et al. 2016).
Efficacy of a drug is not only determined by susceptibility
of cancer cells to the agent, but also (and inversely) by sensitivity of the recipient’s healthy cells to that agent. Hence,
although cancer cell expression of DihydroPyrimidine
Dehydrogenase (DPD) would be expected to reduce the
effectiveness of 5-FU as it metabolizes and clears this agent,
an inverse relationship would be predicted between DPD
expression in non-cancer cells, given that reduced levels
would increase toxicity of the drug. Cancer cell DPD levels
proved ineffective as a biomarker (Elander et al. 2018), but
germline genotypes of up to 9% of patients, which give low
DPD expression do result in higher toxicities and therefore
poor prognosis (Wörmann et al. 2020).
Semi-empirical Biomarker Selection
There are pathways and processes that, while not specific to any
particular agent, are clearly related to an effective response to
chemotherapy. For example, rate of cell cycle progression, ability
to carry out apoptosis, level of inflammation, and capacity for
DNA repair. A cancer that is poor at DNA repair would be predicted to respond better to a DNA damaging agent, conversely a
patient who is poor at DNA repair due to germline deficiency
might be predicted to suffer greater toxicity with such an agent.
An agent that relies on patient’s immune response will be more
likely effective in a patient with an unimpaired immunogenic
capacity or against a particularly immunogenic cancer. Agents
that work through apoptosis will be most effective against cancers where key elements of the apoptotic pathways are intact.
Finally, agents that work by killing dividing cells will be most
effective where cancer cells have a high turnover.
DNA Damage Repair Gene Mutations
PDAC can be subtyped (“stable,” “locally rearranged,” “scattered,” and “unstable”) based on genomic alterations and variation in chromosomal structure (Waddell et al. 2015). DNA
damage repair (DDR) gene mutations commonly associated
with the “unstable” molecular subtype include that carry out
Homologous DNA Repair, generally Fanconi anemia pathway
genes (e.g. BRCA1/2 and PALB2) and genes involved in sig-
naling the presence of DNA damage (e.g. ATM/ATR).
Mutations in these genes can be both germline and somatic in
PDAC (Perkhofer et al. 2021). The unstable group of patients
would be predicted to respond better to DNA damaging
agents such as FOLFIRINOX (Froeling et al. 2021). More specifically, deficiency in homologous DNA repair of double
strand breaks would make cells more sensitive to agents that
target other forms of repair and so increase the chance of double strand breaks forming. Hence in patients who have germline mutations in BRCA1 or BRCA2 their cancer cells are
more sensitive to inhibitors of the proteins PARP1/PARP2
which are involved in base excision repair and other forms of
single strand DNA repair (Bryant et al. 2005). Maintenance
treatment using PARP inhibitors (such as Olaparib) have
shown clinical efficacy in ovarian, breast, and metastatic
pancreatic cancer on a background of BRCA germline mutations. Most significantly, the efficacy of Olaparib as a maintenance therapy was recently assessed in the phase III POLO
(Pancreas Cancer Olaparib Ongoing) trial, which demonstrated extended progression free survival (7.4 months vs 3.8
months for the placebo group) in germline BRCA1/2 mutated,
metastatic PDAC (Golan et al. 2019).
Notably, the established clinical benefit has so far been
restricted to germline BRCA mutations. Up to 7% of PC patients
harbor germline BRCA mutations (Golan et al. 2019); loss of
homologous DNA repair probably occurs earlier in tumorigenesis in such patients and so will be more homogeneous in the
cancer, which may explain why inherited mutations result in the
most obvious benefit from PARP inhibition. Other inherited
mutations in DNA repair genes include pathogenic mutations
in mismatch repair (MMR) genes (MLH1, MSH2, MSH6,
PMS2). Germline mutations in these genes cause Lynch syndrome, which is most associated with a hereditary predisposition to colon cancer, but also increase the risk of PDAC, MLH1
mutation being associated with a 7.8 relative risk of PDAC
(Bujanda and Herreros-Villanueva 2017). The MMR proteins
function to maintain genomic stability by repairing base-base
mismatches or insertion/deletion mis-pairs following DNA replication and recombination. DNA mismatches commonly occur
in regions with repetitive nucleotide sequences (microsatellites);
loss of function of the MMR proteins results in accumulation of
mononucleotide errors, causing microsatellites to change length
(i.e. demonstrate instability).
Pancreatic cancer is generally not associated with microsatellite instability and is not associated with sensitivity to checkpoint inhibitor treatment. However, MSI is associated with
better response to immunotherapy, presumably because of a
greater mutational burden in the cancers (Ganesh et al. 2019).
So, it is reasonable to assume that PDAC in Lynch syndrome
families would be more responsive to checkpoint inhibitors
(Kubo et al. 2022).
The ATM protein, like the ATR protein binds to double strand
DNA breaks and signals to cause cell cycle arrest and DNA
repair. Biallelic pathogenic variants in the ATM gene result in
ataxia telangiectasia, a syndrome including telangiectasia of the
skin, cerebellar ataxia, and immunosuppression. Affected individuals present in the first decade of life and have a 38% risk of
cancer over their lifetime, including a 2.4-fold increased risk of
PC (Geoffroy-Perez et al. 2001; Thompson et al. 2005). There is
some evidence that PDAC with mutant ATM is more sensitive
to gemcitabine, consistent with gemcitabine inhibiting ribonucleotide reductase and hence promoting replication catastrophe
through nucleotide starvation (Dunlop et al. 2020).
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Inflammation, Apoptosis, and the Cell Cycle
While biomarkers of DNA repair defects have proved to be
promising predictive biomarkers (at least with germline mutations) markers of apoptosis and cell cycle progression have
proven more effective as prognostic markers than predictive
markers. A mutation in TP53, for example, will make a cell more
resistant to gemcitabine (Fiorini et al. 2015) as it is more difficult
to induce apoptosis, but it will similarly reduce the sensitivity to
FOLFIRINOX or any other chemotherapy, so it is of limited predictive value when selecting a therapy. Similarly, inflammatory
markers may associate with worse survival in patients treated
with irinotecan, there is as yet no evidence that better survival
would be seen with alternative treatments (Merz et al. 2020).
Predicting Response to Novel Agents
To date chemotherapy remains the mainstay of adjuvant, neoadjuvant, and palliative care. This is no doubt a consequence of the
limited number of patients who will benefit from more targeted
therapy, hence an inability to show benefit in an unselected
group of patients. Predictive biomarkers could provide the
necessary selection of necessity; this will require an even greater
dependence on non-empirical methods to choose which
markers to test. Fortunately, targeted therapy has (by definition)
a clear target and equally clear candidate biomarkers.
Perhaps the most obvious target for treatment of pancreatic
cancer is the near ubiquitous K-Ras mutations. These have
proven remarkably difficult to exploit, but recently anti KRAS
agents (sotorasib and adagrasib) have been FDA approved for
treating KRAS G12C mutated tumors, although this variant
constitutes only 3% of PDACs (Hong et al. 2020).
Predictive Biomarkers in Systemic PDAC Therapy
Predicting patient response to these agents in advance of
therapy is therefore pivotal for optimizing clinical outcomes.
Dihydropyrimidine dehydrogenase (DPD) is a key catabolic
enzyme of 5-FU, and chemo resistance to this agent is seen with
its upregulation, resulting in poor clinical response (Elander
et al. 2018). Guidelines therefore recommend patient testing
for DPD deficiency prior to treatment with 5-FU derivatives
and capecitabin (Wörmann et al. 2020).
Biliary Tract Cancers
preventing these cancer types (Valle et al. 2017). BTCs are
defined as epithelial malignancies of the biliary tract and
include cancers of the gallbladder (GBC), cholangiocarcinoma
(CCA) or bile duct cancer, affecting intrahepatic (iCCA) or
extrahepatic bile ducts (eCCA), and the ampulla of Vater
(AVC) (Jackson et al. 2019).
BTC incidence varies by geographic regions, with the highest
incidence observed in Chile (14.35) and the lowest in Vietnam
(1.25) (Baria et al. 2022). BTCs occur more commonly in
South-East Asia, due to the increased incidences of parasitic
diseases including liver flukes Opisthorchis viverrini (OV) and
Clonorchiasis sinensis (CS) infestations in those zones (particularly in north-east Thailand and China) where BTCs (specifically CCA) are more prevalent (Sithithaworn et al. 2012).
However, overall, GBC is the most common cancer of the biliary tract (2.2 cases per 100,000 worldwide) although incidence
varies globally (Mahdavifar et al. 2018; Marcano-Bonilla et al.
2016). The incidence of BTC in high-income countries are
about 0.35–2 cases per 100,000 annually; however, in endemic
regions of Thailand and China its incidence increases up to
40-fold (Izquierdo-Sanchez et al. 2022; Valle et al. 2021). A
higher prevalence is observed in certain regions (South
America and southern Asia) where cholelithiasis is more prevalent and linked with the development of GBC (Hundal and
Shaffer 2014). The incidence of GBCs is lower in western
Europe and the USA (1.6–2.0/100,000) and in high-risk countries (e.g., Chile and India) the incidence reaches 24.3/100,000
in females (and 8.6/100,000 in males) (Rawla et al. 2019).
Overall, the incidence of BTCs is higher in males than in
females (Baria et al. 2022).
Currently, CCA is the second most common type of primary
hepatic malignancy, accounting for an estimated 15% of all primary liver tumors (Sung et al. 2021). CCA contributes roughly
to 2% of all cancer-related deaths yearly. Epidemiological data
suggests a constant rise in CCA incidence worldwide, being
distinctly apparent in certain Western and Southeast Asian
countries, with Asians having the highest incidence for both
subtypes (Banales et al. 2016). Worldwide, the highest CCA
incidence rate is found in Thailand (113/100,000 in males, and
50/100,000 in females). Globally, CCA has an incidence rate of
0.3–6/100,000 inhabitants per year (Ledbetter and Hotchkiss
1975). Studies have reported differences in the trends in incidence among iCCA and eCCA, as iCCA is seen to be on a
steady rise, whereas eCCA displays a more stable incidence rate
(Brindley et al. 2021; Khan et al. 2019).
Introduction
Biliary tract cancers (BTCs) comprise a group of rare and
highly heterogeneous malignancies associated with poor
patient survival, and a poorly understood etiology (Banales
et al. 2020; Tariq et al. 2019). Identifying key modifiable risk
factors in BTCs is essential for predicting, treating, and
Etiology and Risk Factors
The etiology of BTCs is complex and remains poorly understood. Main risk factors are common among BTCs (Table 1).
Presence of gallstones is the strongest risk factor for GBC and is
also relevant to CCA development (hepatolithiasis) (Kirstein
and Vogel 2016). Other risk factors include porcelain
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Table 1 Summary of risk factors and high-risk groups linked with the development of pancreatic and biliary tract cancers.
Risk factor Pancreatic cancer BTC
Lifestyle
Genetic predisposition Germline mutations:
Environmental,
occupational
High risk groups Familial Pancreatic Cancer
Anatomical,
abnormalities
Modifiable
Smoking
Alcohol
Obesity
Dietary
• High fructose diet
• Red meat
• Processed meats
Non-modifiable
Age
• BRCA1/2 (HBOC)
• KRAS
• PALB2
• CDKN2A
• ATM
• APC
• MLH1/MSH2, MSH2, MSH6 (Lynch Syndrome)
• TP53 (Li-Fraumeni syndrome)
Pesticides
Aromatic Hydrocarbons
Heavy metals
Hereditary Caner Syndromes
• Peutz-Jeghers Syndrome (STK11)
• Hereditary pancreatitis (PRSS1)
Pancreatic Cysts
• IPMNs
• MCNs
New-Onset Diabetes (NOD)
Modifiable
Smoking
Alcohol
Obesity
Dietary
• Low fiber diet
• Raw fish
• Red meat
• Processed meats
Germline mutations:
• BRCA1/2
• KRAS
• SMAD4
• NF1
• ATR
• NF1
Toxins
• Aflatoxin (Aspergillus spp)
• Ochratoxin A (Aspergillus spp)
Parasitic infections
• O. viverrine
• C. sinensis
Viral infections
• Hepatitis B virus (HBV)
• Hepatitis C virus (HCV)
Bacterial infections
• Salmonella spp
• H. pylori
Primary Sclerosing Cholangitis (PSC)
Liver fluke Endemic regions
• (O. viverrini, C. sinensis)
High risk occupation
• Rubber, dye industry
• Agriculture (pesticides)
Gallbladder polyps
Caroli’s disease
Gallstones, hepatolithiasis
Porcelain gallbladder
• ARID1A
• PBRM1
• RAD51D
• MUC17
• MLH1/MSH2
• TP53
spp: Species; NOD: New Onset Diabetes.
gallbladder, gallbladder polyps, benign biliary disorders associated with chronic inflammation (such as primary sclerosing
cholangitis (PSC) for CCA), chronic infection (e.g. salmonella
typhi), liver diseases (cirrhosis due to other causes, hepatitis C),
congenital malformations such as choledochal cysts and biliary
papillomatosis, family history (risk increases if multiple
first-degree relatives had the disease), and genetic alterations
(e.g. mutations in TP53, KRAS, CDKN2A, IDH1/2, FGFR2
fusions). Apart from geographically associated risks, there are
some other risk factors associated with an increased risk of
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developing BTCs which are a bit more controversial, including:
age, metabolic disorders (diabetes, obesity), smoking, and
alcohol abuse (Labib et al. 2019). However, in the case of CCA,
the vast majority of cases (around 70%) develop sporadically
without any known underlying disease (Brindley et al. 2021;
Izquierdo-Sanchez et al. 2022; Khan et al. 2019).
Infectious Diseases
Parasitic Infections
Certain systemic infections involving parasites, bacteria, and
viruses play a pivotal role in the development of BTC through
longstanding inflammation within the biliary tract. There is
a strong association between Opisthorchis viverrini (OV) and
Clonorchiasis sinensis (CS) liver fluke infestations and the
development of biliary tract malignancies (Prueksapanich
et al. 2018). There is a consistently high incidence of CCA in
endemic regions including Asia and North-Eastern Europe
areas with an incidence of 85 per 100,000 population
(Chaiteerakij et al. 2017). O. viverrini infection, is endemic to
northeast Thailand, Laos, and Cambodia, whereas China,
Taiwan, Korea, and Vietnam are endemic areas for C. sinen-
sis, both which are risk factors most strongly associated with
development of CCA (Khan et al. 2019). The consumption of
raw or undercooked wild-caught cyprinoid fish (which host
these parasites) leads to infestation with these liver flukes
which drive an inflammatory process whilst occupying the
biliary tree and the gallbladder (Moeini et al. 2021; Phyo
Myint et al. 2020). These flukes secrete oncogenic metabolites into circulating bile which are inflammatory as they
induce desquamation of the biliary epithelium, ductal
fibrosis, and adenomatous hyperplasia formation within biliary ducts which precedes dysplasia and biliary carcinoma
(Cardinale et al. 2018). Evidence of fluke-induced host
inflammatory response within biliary ducts is evident
through the expression of Opistorchis and Clonorchiasis
antigens in circulating peripheral macrophages and epithelioid cells of the host’s immune systems (Brindley et al. 2021).
Subsequent to epithelial injury and an immune response
against these parasites, pro-inflammatory cytokines and
transforming growth factors activate fibroblasts, and proteases and metalloproteinases are released (which induce
changes in the extracellular matrix and oxidative stress)
leading to further injury and fibrosis of the biliary tract. This
is the initial step in a multistep process that involves the
transformation from hyperplasia to dysplasia and eventually
carcinogenesis (Sala et al. 2020; Zhao et al. 2021).
Viral Infections
The persistent infection with Hepatitis B (HBV) and Hepatitis
C (HCV) viruses contributes to the pathogenesis of BTC. The
prevalence of chronic viral infections coincides with the
highest prevalence of CCA, which we see in Southeast Asia
when compared to Western countries (UK, USA, Denmark,
Norway, and the Czech Republic) (Wang et al. 2017). The prevalence of HBV and HCV in southeast Asia is 9.1% and 3.6%
respectively; when looking at specific countries in this region,
China (HBV 12%; HCV 3%), and Korea (HBV 12%; HCV 2%)
were all higher when compared to western counterparts
(Alberts et al. 2022). Multiple studies have highlighted that
chronic HCV infection increases the risk of iCCA by two-fold
(HR: 2.6; 95% CI: 1.3–5.0) (Marcano-Bonilla et al. 2016) and
that HCV infection is associated with a six-fold odds ratio (OR)
increase in developing iCCA, whilst eCCA risk is unchanged
(Clements et al. 2020). When assessing the risk associated with
chronic HBV, there is a clear increased risk of developing iCCA
(RR: 3.4; 95% CI: 2.5–43.7) when compared to those without
HBV (Fragkou et al. 2021; Marcano-Bonilla et al. 2016). A
meta-analysis looking at the prevalence of HBV in regions of
both high and low incidences of hepato-pancreaticobiliary
malignancies, concluded that chronic HBV infection was associated with an increased risk for iCCA (Bridgewater et al.
2014). The association between HBV and CCA was stronger in
Asian countries (OR: 6.0) compared to Western countries (OR:
4.0) (Marcano-Bonilla et al. 2016). Cirrhosis subsequent of
HBV or HCV persistent infections increases the risk for iCCA,
by 3-fold in HBV patients, and 3.2-fold in HCV patients (Pinter
et al. 2016). In co-infections with both HBV and HCV and an
established cirrhosis, the risk of iCCA significantly increases to
just over 10-fold, hence there is a clear and strong association
between viral hepatitis and CCA (Wang et al. 2017).
Bacterial Infections
Certain bacterial infections of the biliary system occur due to
translocation through disruptions of the intestinal mucosa (Masia
and Misdraji 2018). Bacterial pathogens like Salmonella enterica
subspecies, including S. typhi, a gram-negative bacterial pathogen, can spread to the gallbladder via the vasculature or through
the bile ducts from the liver via the enterohepatic circulation
(Sharma et al. 2022). Several studies have shown that chronic carriers of S. typhi have an increased risk of GBC (Gunn et al. 2014;
Koshiol et al. 2016). The presence of chronic bacterial infection of
similar bacterial pathogens such as the above permits chronic and
persistent inflammation of the biliary epithelium, which can
often potentiate carcinogenesis within the biliary epithelia
through initial hyperplasia, then dysplasia and to the development
of either GBC or other BTCs (Espinoza et al. 2016).
Helicobacters are gram-negative bacteria with a characteristic
helical shape which can act as an infectious carcinogen. Specific
species of helicobacter have been identified within the GI tract
and biliary tract systems in certain hepatobiliary diseases
(Kusters et al. 2006). Similar to the pathogenesis of biliary and
gallbladder malignancies with S. typhi as the infectious carcinogen, studies have shown that H. pylori behaves like S.typhi in
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the development of biliary and gallbladder malignancies
through the promotion, propagation, and maintenance of
inflammation, development of hyperplasia of biliary epithelial
cells which can then develop into dysplasia and eventual biliary
malignancy (Cherif et al. 2019). Virulence factor CagA which is
produced by H. pylori is an oncoprotein that interferes with
signal transduction pathways including activation of the
nuclear factor kappa B (NF-B) signaling pathway. NF-B signaling induces cellular expression of the pro-angiogenic
vascular endothelial growth factor (VEGF) which is implicated
in BTC and GBC development (Ito et al. 2020; Stein et al. 2013).
The oncoprotein also promotes the host immune response to
Helicobacter antigens in the form of an inflammatory response
with a milieu of cytokines and other inflammatory mediators
(Segura-López et al. 2015). Numerous studies have supported
the conclusion of clear associations between H. pylori and the
development of hepatobiliary cancers, including BTCs and
GBCs (Makkar et al. 2020).
Biliary Tree Structural Abnormalities
Certain anatomical defects are also associated with an increased
risk for CCA and GBC. Choledochal cysts (bile-filled sacs along
the common bile duct) and Caroli’s disease (rare congenital
fibro-polycystic dilations within the large intrahepatic bile ducts)
both contribute to the increased risk of the development of iCCA
(Jabłońska 2012; Ye et al. 2022). Caroli’s disease increases the risk
of developing CCA by 100-fold when compared to the general
population (Fahrner et al. 2020). In respect to cases of congenital
choledochal cysts, these cysts often develop in an abnormal pancreaticobiliary junction (APBJ) in 90% of cases of congenital
choledochal cysts. The APBJ is a rare anatomical variation, this
results in the main pancreatic duct draining directly into the
common bile duct (Baison et al. 2019). This can potentiate
chronic and prolonged exposure and irritation of the biliary epithelium by pancreatic juice (which contains digestive enzymes
including trypsinogen, elastase, carboxypeptidase, lipases, amylase) (Kamisawa et al. 2017). The resulting chronic inflammation
promotes the carcinogenesis of both GBC and BTCs. Studies
identified however that APBJs more commonly underly cases of
GBC than CCA (Hu and Lim 2022).
Gallstones and Hepatolithiasis
Gallstones are a common risk factor for the development of
GBC. Cholesterol and pigment gallstones are synthesized
within the gallbladder and can cause chronic inflammation via
chronic cholecystitis, although the exact mechanism by which
gallstones predispose GBC is still unknown. About 80% of people with GBC present with gallstones during diagnosis, however, the majority of patients with gallstones do not develop
GBC (Stinton and Shaffer 2012). The association of gallstones
with CCA is less well established than the association of
gallstones with GBC, however, the association of bile duct
stones or hepatolithiasis with CCA is quite strong and can be
due to chronic inflammation of the bile duct (Kirstein and
Vogel 2016). Hepatolithiasis is a well-known associated risk
factor for CCA development, particularly in parts of SouthEast Asia (Kim et al. 2015). Studies have highlighted that one of
the highest associated risks between CCA and hepatolithiasis is
in Japan (Cardinale et al. 2010). Hepatolithiasis are caused by
persistent portal bacteremia and associated vessel phlebitis.
Their presence leads to obstruction of the intrahepatic biliary
ducts causing bile stasis and reflux, therefore increasing the risk
of cholangitis and bile stricture formation (Jarnagin and
Winston 2005). This chronic inflammatory process drives the
development of CCA (Khan et al. 2019).
A recent 30-year follow-up study concluded that BTC risks
were significantly higher in patients who had both gallstones
and cholecystitis, in regards to developing GBC (OR = 34.3,
95% CI 19.9–59.2) (Hsing et al. 2007). The type of gallstone can
delineate the likelihood of the occurrence of either BTC or
GBC. A study underlined that patients with bile duct cancer
were more likely to have pigment stones, and patients with
GBC to have cholesterol stones (Portincasa et al. 2019). Overall,
gallstones were associated with a higher risk of cancers of the
hepatopancreaticobiliary system.
Porcelain Gallbladder
The porcelain gallbladder is associated with an increased risk
of developing GBC. A porcelain gallbladder is a condition
whereby the wall of the gallbladder becomes covered with
calcium deposits. This can also occur as a result of chronic
recurrent cholecystitis caused by gallstones (Calomino et al.
2021). Patients with porcelain gallbladder have a higher risk of
developing gallbladder cancer likely due to persistent inflammation associated with the pathogenesis in the development of
this condition (Morimoto et al. 2021).
Gallbladder Polyps
A gallbladder polyp is essentially an exophytic growth from
within the inner surface of the gallbladder wall. The constituents of some of the polyps are directly formed from cholesterol
deposits lining the gallbladder wall, some of the polyps are
derived from inflammation, and the remaining polyps are a
benign or malignant aggregation of biliary epithelium (AndrénSandberg 2012). Large polyps (>1cm) are likely to be cancerous
and often the recommendation is for cholecystectomy for
polyps of this size or larger (Kalbi et al. 2021).
Primary Sclerosing Cholangitis
Primary sclerosing cholangitis (PSC), is the most well-known
associated risk factor concerning CCA. PSC is an autoimmune
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