Abstract
Neoadjuvant chemoradiotherapy (nCRT) has become the standard treatment
for patients with locally advanced rectal cancer (LARC). Therapeutic
efficacy of nCRT is significantly affected by treatment-induced
diarrhea and hematologic toxicities. Metabolic alternations in cancer
therapy are key determinants to therapeutic toxicities and responses,
but exploration in large-scale clinical studies remains limited. Here,
we analyze 743 serum samples from 165 LARC patients recruited in a
phase III clinical study using untargeted metabolomics and identify
responsive metabolic traits over the course of nCRT. Pre-therapeutic
serum metabolites successfully predict the chances of diarrhea and
hematologic toxicities during nCRT. Particularly, levels of acyl
carnitines are linked to sex disparity in nCRT-induced diarrhea.
Finally, we show that differences in phenylalanine metabolism and
essential amino acid metabolism may underlie distinct therapeutic
responses of nCRT. This study illustrates the metabolic dynamics over
the course of nCRT and provides potential to guide personalized nCRT
treatment using responsive metabolic traits.
Subject terms: Metabolomics, Colorectal cancer, Mass spectrometry
__________________________________________________________________
Neoadjuvant chemoradiotherapy is the standard treatment for patients
with locally advanced rectal cancer, however, response can be limited
by development of toxicities. Here, the authors conducted metabolomics
on patients with locally advanced rectal cancer enrolled in a phase III
clinical study and identify serum metabolites associated with treatment
response.
Introduction
Colorectal cancer (CRC) is the third most commonly diagnosed cancer and
the fourth leading cause of cancer-related deaths worldwide^[45]1. Up
to 15% CRC patients had locally advanced rectal cancer (LARC) at the
time of diagnosis, which is of great concern as patients with LARC are
at high risk for postoperative local recurrence and distant
metastasis^[46]2–[47]4. Neoadjuvant chemoradiotherapy (nCRT) followed
by total mesorectal excision (TME) is the standard treatment for
patients with LARC^[48]5. Patients with complete clinical response
(cCR) after nCRT can follow the watch-and-wait strategy^[49]6. This
strategy provides LARC patients with the opportunities to preserve the
anus without radical surgery and improves life qualities. However,
patients vary considerably in response to nCRT and individualized
therapeutic strategies are therefore imperative needs for their
treatments. Clinical statistics showed that only 10–35% patients can
achieve pathological complete response (pCR)^[50]7. As a comparison,
40% to 45% patients present tumor regression with different grades,
while the rest 20–30% have no responses to nCRT at all and such
patients have to undergo total mesorectal excision surgery
immediately^[51]8,[52]9. In clinical practice, presurgical examinations
including magnetic resonance imaging, endoscopy, and digital rectal
examination have been used to evaluate the efficacy of nCRT
treatment^[53]10. But these methods are insufficient to provide
accurate evaluations, thereby impeding personalized therapy amid the
nCRT treatment of LARC patients.
Therapeutic responses of LARC patients to nCRT treatment depend on many
genetic and clinical factors. Over the course of nCRT treatment, LARC
patients experience adverse effects such as leukopenia, neutropenia,
and diarrhea^[54]11–[55]14. The treatment-induced toxicities are
responsible for cessation of treatment for patients who are unable to
tolerate high dose of radio- and chemo- therapies. Therefore, adverse
events are key determinants of nCRT therapeutic responses and ultimate
clinical outcomes. For example, a multicenter and randomized phase III
study showed that up to 20–36% LARC patients who received nCRT
treatments presented with preoperative grade 3–4 toxic effects^[56]14.
The treatment-related adverse events led to radiotherapy interruption
in 7–10% patients, radiotherapy dose reduction in 3% patients, and
concurrent chemotherapy dose reduction in 15–21% patients depending on
the drugs used^[57]14. It is also evident that interpatient variability
of adverse events is commonly present in LARC patients treated with
nCRT. For example, grade 3–4 diarrhea, stomatitis, and alopecia were
significantly more frequent in females than in males treated with
FOLFIRI (a combination therapy consisting of leucovorin, fluorouracil,
and irinotecan)^[58]15. But the reason for this sex-specific difference
is unknown. In addition, LARC patients vary in the risk of severe
neutropenia from the treatment of irinotecan based nCRT, which is
suggested to be related in part to UGT1A1*28 (uridine diphosphate
glucuronosyltransferase 1A1*28), a genetic variant that reduces the
elimination of metabolic product of irinotecan^[59]16,[60]17.
Therefore, a variety of factors play critical roles in clinical
outcomes for LARC patients with nCRT treatment. It is thus imperative
to characterize the systemic effects of nCRT treatment and in
particular to predict the toxic effects prior to therapy, with the
ultimate goal of minimizing adverse effects and improving patient
benefits.
Metabolomics provides comprehensive measurements of metabolites in
biological systems and offers molecular insights towards pathological
phenotypes^[61]18,[62]19. It is widely recognized that metabolic
dysregulation contributes to colorectal cancer, including through
aberrant glycolysis, glutaminolysis, one-carbon metabolism, and fatty
acid synthesis^[63]20–[64]22. However, exploration of responsive
metabolic traits in the context of neoadjuvant chemoradiotherapy
remains limited. Though metabolism alterations induced by chemotherapy
or radiotherapy have been reported in animal models^[65]23,[66]24,
metabolomics studies on large-scale clinical cohorts of LARC patients
with nCRT treatment are rather limited^[67]25,[68]26. In this work, we
performed untargeted metabolomics for the CliClare study to reveal
metabolic traits in response to therapeutic toxicities and efficacy of
nCRT in patients with rectal cancer. The CliClare study is a
multicenter, randomized, phase III clinical trial to evaluate the use
of the UGT1A1 genotype to guide the irinotecan dose when used in
combination with capecitabine-based nCRT in LARC patients
(ClinicalTrials.gov identifier: [69]NCT02605265)^[70]27. We
demonstrated that the pCR rate was significantly increased from 15% to
30% using the capecitabine and irinotecan-based treatment regime
compared to capecitabine-based regime, however, increases in the
frequency of grade 3–4 toxicities were also observed. Here, we analyzed
serum samples (n = 743) from 165 LARC patients in CliClare study over
their nCRT treatments, and identified responsive metabolic traits that
were correlated with nCRT treatment. We further investigated the links
between serum metabolic traits and adverse toxicity effects induced by
nCRT, including diarrhea and hematologic toxicity. Particularly, we
found that levels of acyl carnitines are linked to sex disparity in
diarrhea induced by nCRT. Finally, we examined the metabolic
alterations over the course of nCRT treatment and clinical outcomes,
and discovered significant differences in gut microbiota related
phenylalanine metabolism and essential amino acid metabolism between
pCR and non-pCR patients. Altogether, our results reveal the metabolic
dynamics over the course of nCRT treatment and provide great potentials
to guide personalized nCRT treatment using responsive serum metabolic
traits.
Results
Serum metabolic traits in response to nCRT in LARC patients
In this study, we recruited 165 patients with clinical T3-4 and/or
N + rectal cancer diagnosed from Fudan University Shanghai Cancer
Center (FUSCC), Shanghai, China. All enrolled patients were treated
with the standard neoadjuvant chemoradiotherapy (nCRT) protocol
followed by total mesorectal excision surgery (see Methods section).
The detailed information on the clinical cohort is listed in
Supplementary Table [71]1. To investigate the metabolic dynamics over
the course of nCRT, serum samples for each patient were collected at
the following time points: before nCRT (Time 1), at the 5th fractions
of nCRT (Time 2), at the 15th fractions of nCRT (Time 3), at the 25th
fractions of nCRT (Time 4), and after the rest for two months and
within 2 days before surgery (Time 5) (see Fig. [72]1a and
Supplementary Fig. [73]1).
Fig. 1. Serum metabolic traits in response to nCRT in LARC patients.
[74]Fig. 1
[75]Open in a new tab
a Overview of the clinical design of the metabolomics study. b Number
of identified metabolites in serum samples and distributions of
chemical classes. c Principal component analysis (PCA) shows
significantly altered metabolic profiles over the course of nCRT. Each
dot represents the averaged value from all patients. d Metabolites were
significantly associated with the nCRT treatment analyzed by SAM
(Significance Analysis of Microarrays; FDR adjusted P < 0.05). Red dots
(n = 119) and blue dots (n = 100) represent metabolites that were
increased and decreased, respectively, over the course of nCRT. The
black dots represent unchanged metabolites. e Pathway enrichment
analysis using significantly changed metabolites (n = 219) associated
with nCRT. (Hypergeometric test; P < 0.05). The pathway enrichment
analysis was performed using MetaboAnalyst. f, g Hierarchical
clustering analyses (HCA) using the top 50 increased (f) and top 50
decreased (g) metabolites over the course of nCRT.
Comprehensive untargeted metabolomics analyses using hydrophilic
interaction liquid chromatography-mass spectrometry (HILIC−MS) and
reversed-phase liquid chromatography-mass spectrometry (RPLC−MS) were
performed on serum samples from locally advanced rectal cancer (LARC)
patients (n = 743 in total). In total, 557 metabolites were identified
on the basis of the Metabolomics Standards Initiative (MSI)^[76]28
(Fig. [77]1b and Supplementary Data [78]1). As a result of the
unsupervised principal component analysis (PCA), metabolic profiles of
the five-time point serum samples were markedly different, with the
metabolome being changed over the course of nCRT (Fig. [79]1c). Next,
we used the SAM method^[80]29 (Significance Analysis of Microarrays) to
identify metabolites that associate with the nCRT treatment. A total of
219 metabolites were significantly changed over the course of nCRT (FDR
adjusted P < 0.05; Supplementary Data [81]2). Among them, the
quantities of 119 metabolites were increased and 100 metabolites were
decreased over the course of nCRT (Fig. [82]1d). Metabolic pathway
enrichment analysis revealed that the 219 altered metabolites were
represented in amino acid metabolism including valine, leucine and
isoleucine biosynthesis, arginine biosynthesis, and glycine, serine and
threonine metabolism (Fig. [83]1e).
Further examination using hierarchical clustering analysis (HCA) showed
that the nCRT induced alterations in metabolic dynamics were
distinctive among the changed metabolites (Fig. [84]1f, g). For
metabolites that were increased during nCRT, two different clusters
were generated. Levels of metabolites in cluster 1 such as hypoxanthine
and uracil were increased with the number of nCRT fractions, whereas
the majority of metabolites in cluster 2 had little changes until the
25th nCRT. Similarly, two clusters (i.e., clusters 3 and 4) were
observed for metabolites that were decreased over the course of nCRT.
Levels of metabolites in cluster 3 were decreased significantly at the
15th nCRT and maintained the lower levels until surgery (Time 5). For
examples, metabolites in tryptophan metabolism decreased significantly
after nCRT (Supplementary Fig. [85]2). In contrast, metabolites in
cluster 4 such as ornithine and citrulline decreased at the 15th nCRT,
but before surgery, levels of these metabolites restored to the
baseline (Time 1). Taken together, comprehensive metabolomics
demonstrated that nCRT treatment induced significant changes in
metabolic profiles and identified responsive metabolic traits over the
course of nCRT in LARC patients.
Acyl carnitines are linked to sex disparity in diarrhea induced by nCRT
Diarrhea is one of the most common adverse events induced by
neoadjuvant chemoradiotherapy in locally advanced rectal cancer
patients, which compromises the treatment efficacy and clinical
outcomes^[86]12,[87]30. To investigate metabolic traits associated with
diarrhea induction, we analyzed the metabolomics data at baseline (Time
1) from patients with (n = 25) and without diarrhea (n = 30) before
they received nCRT. Initial analysis identified 21 metabolites that
were associated with diarrhea (P < 0.05; Wilcoxon test; Fig. [88]2a).
Pathway analysis showed that these metabolites were significantly
enriched in glyoxylate and dicarboxylate metabolism, and phenylalanine,
tyrosine and tryptophan biosynthesis (P < 0.05; Hypergeometric test;
Fig. [89]2b). Next, we used adaptive lasso regression to select key
metabolites and constructed a logistic regression model to predict the
chance of diarrhea in LARC patients before they received nCRT. Three
metabolites, serine, uridine, phenylalanine, and the clinical covariate
of sex were selected out for the prediction model (Fig. [90]2c and
Supplementary Table [91]2). The cut-off score for prediction was 0.50
with the diagnostic sensitivity of 90% and the specificity of 80%
(Fig. [92]2d). The receiver operating characteristic (ROC) curve also
confirmed the good discriminative performance (AUC = 0.88; 95% CI:
0.77–0.98, Fig. [93]2e). Of note, this model enabled to predict LARC
patients with all levels of diarrhea (Supplementary Fig. [94]3a). Based
on the sample sizes of 25 cases and 30 controls, we found that all of
four factors had sufficient powers for diarrhea prediction (powers of
four factors in model larger than 0.8 with α = 0.05; Supplementary
Table [95]3)^[96]31. These results showed that the pre-therapeutic
metabolic traits are markedly different between patients with and
without the nCRT induced diarrhea, and metabolite levels prior to
therapy have potential for identifying individuals who are at the high
risk of nCRT toxicity.
Fig. 2. Acyl carnitines are linked to sex disparity in diarrhea induced by
nCRT.
[97]Fig. 2
[98]Open in a new tab
a Pre-therapeutic metabolites in baseline were significantly associated
with diarrhea of LARC patients (P < 0.05; Two-sided Wilcoxon test). All
metabolites were corrected by the covariate of sex using a linear
model. b Pathway enrichment analysis (P < 0.05; Hypergeometric test). c
Selected key metabolites and the covariate of sex for the logistic
regression model to predict the chance of diarrhea. d Sensitivity and
specificity of the prediction model with the risk score of 0.5
(Diarrhea: n = 25; No diarrhea: n = 30). e Receiver operating
characteristic curve of the logistic regression model. f Female
patients had a significant higher chance of diarrhea than males
(P < 0.05; χ^2 test). g, h Levels of total carnitines and Car (20:4)
between females and males in all patients (Female: n = 17; Male:
n = 38) and patients with diarrhea (Female: n = 12; Male: n = 13). The
centerline of boxes depicts the median values; the bottom and top box
edges correspond to the first and third quartiles, and whiskers
indicated the minimum and maximum values. i Sex disparity of carnitine
levels related to diarrhea induced by nCRT (nd., not detected; **,
P < 0.01; *, P < 0.05; ns., not significant; Two-sided Wilcoxon test).
Previous studies have reported the sex-related differences in the nCRT
induced adverse events such as diarrhea, with the female sex being a
risk factor^[99]15. In our study, a significant higher incidence of the
induced diarrhea was also observed in female patients than in male
patients (P < 0.05; Chi-square test; Fig. [100]2f). Then, we sought to
investigate the sex-specific differences in metabolic traits related to
the nCRT induced diarrhea. Comparative analyses of metabolites between
female and male patients with diarrhea revealed that 29 metabolites
were associated with sex and diarrhea (P < 0.05; Wilcoxon test). Among
them, 13 metabolites showed no differences between all female and male
patients while the diarrhea factor was unstratified (Supplementary
Fig. [101]3b), which suggests that these metabolites have
sex-specificity in patients with diarrhea only. Closer examination
revealed that three carnitines (i.e., Car (12:2), Car (13:0) and Car
(20:4)) were significantly linked with the sex-specific differences in
the nCRT induced diarrhea. Interestingly, total serum carnitine summed
by 34 detected carnitines had a significantly higher level in male
patients with diarrhea than that in female patients (Fig. [102]2g and
Supplementary Data [103]3). As a comparison, this sex disparity in
total carnitine level was not observed for all patients combined. For
example, Car (20:4) has been found to display a higher level in males
with diarrhea than that in females with diarrhea, while no difference
was observed between the two sexes for all patients combined
(Fig. [104]2h). The similar results for Car (12:2) and Car (13:0) were
provided in Supplementary Fig. [105]3b. The comparison for the levels
of total carnitine and individual carnitines among four groups (males
with diarrhea, males without diarrhea, females with diarrhea, females
without diarrhea) was showed in Supplementary Fig. [106]4. Furthermore,
subsequent analyses of carnitines on the basis of carbon number and
unsaturation degree revealed additional sex-related differences. We
found that saturated long chain carnitines (C > 12) had significant
differences between males and females with diarrhea, whereas saturated
middle chain carnitines (5 ≤ C ≤ 12) showed no sex differences
(Fig. [107]2i). Middle chain carnitines with two carbon-carbon bonds
and long chain carnitines with high degree of unsaturation (≥3) held
sex differences in the nCRT induced diarrhea. Combined, these results
clearly showed that the sex-specific differences in metabolism is
linked to the diarrhea induced by nCRT.
Pre-therapeutic metabolic traits predict the nCRT induced hematologic
toxicities
Hematologic toxicity frequently occurrs in locally advanced rectal
cancer (LARC) patients who received neoadjuvant chemoradiotherapy
(nCRT), which also contributes to cessation of treatment for patients
who are unable to tolerate high dose of radio- and chemo- therapies,
and reduces the nCRT therapeutic efficacy^[108]13,[109]14. We next
examined how the pre-therapeutic metabolite levels prior to nCRT
(baseline; Time 1) are correlated with common hematologic toxicities,
such as the lowest cell counts of white blood cells (WBC), neutrophils
(NEUT), and blood platelet cells (BPC), and the lowest level of
hemoglobin (Hb). These values were measured for individual patients
during nCRT. We identified that 135 metabolites from different chemical
classes had significant correlations with at least one of the
hematologic toxicities (Pearson correlation; P < 0.05; Fig. [110]3a and
Supplementary Data [111]4). Pathway enrichment analyses showed that the
WBC, NEUT, and Hb correlated metabolites were significantly enriched in
similar metabolic pathways, such as citrate cycle and aminoacyl-tRNA
biosynthesis (Fig. [112]3b). As a comparison, BPC correlated
metabolites were highly enriched in galactose metabolism and amino
sugar and nucleotide sugar metabolism (Fig. [113]3b). As shown in
Fig. [114]3c, we demonstrated additional metabolite examples including
nicotine, thyroxine, creatinine, and asymmetric dimethylarginine (ADMA)
and their correlations with hematologic toxicities.
Fig. 3. Pre-therapeutic metabolic traits predict the nCRT induced hematologic
toxicities.
[115]Fig. 3
[116]Open in a new tab
a Significant correlations between pre-therapeutic serum metabolites
(n = 135) and cell counts that indicate hematologic toxicity (Pearson
correlation; two-sided Student’s t test P < 0.05). WBC, white blood
cells; NEUT, neutrophils; BPC, blood platelet cells; Hb, hemoglobin. b
Pathway enrichment analyses of hematologic toxicity associated
metabolites (Hypergeometric test; P < 0.05). c Four metabolite examples
and their correlations with hematologic toxicities. ADMA represents
asymmetric dimethylarginine (Pearson correlation; Two-sided Student’s t
test P < 0.05). Error bands represent 95% confidence intervals. d
Multiple linear regression-based models using baseline metabolites in
serum to predict lowest cell counts and hematologic toxicity during
nCRT (Pearson correlation; Two-sided Student’s t test P < 0.05).
Details of prediction models were provided in Supplementary
Tables [117]4–[118]7. Error bands represent 95% confidence intervals.
Since all metabolites were measured at baseline prior to nCRT, we next
sought to predict hematologic toxicities using the dysregulated
metabolites for LARC patients. Similar to the diarrhea prediction, we
first selected key metabolites and constructed the prediction models
using multiple linear regression (MLR) models. As a result, 15, 12, 12,
and 17 metabolites were selected, respectively, for the prediction
models of WBC, NEUT, Hb, and BPC values (Fig. [119]3d and Supplementary
Tables [120]4–[121]7). Each of the generated prediction models
successfully predicted the WBC, NEUT, Hb, and BPC values using the
baseline metabolite levels before patients received nCRT, and had
significant and positive correlations with the clinically measured
values during nCRT treatment (Fig. [122]3d). Altogether, we
demonstrated that pre-therapeutic metabolic traits in the serum of LARC
patients could successfully predict the chances of various hematologic
toxicities during nCRT, which further indicated that serum metabolic
profiles of LARC patients are key determinates of the nCRT induced
toxic events.
Phenylalanine metabolism underlies distinct therapeutic responses of nCRT
With the completion of neoadjuvant chemoradiotherapy, approximately 10%
to 35% of patients achieve pathological complete response (pCR) and
could be selected for the watch-and-wait strategy while the non-pCR
patients are guided to the TME surgery immediately^[123]32. To
investigate the metabolic alterations reflective of therapeutic
responses over the course of nCRT, we analyzed the metabolic profiles
at baseline (Time 1) and after 25th nCRT (Time 4) for pCR (n = 38) and
non-pCR (n = 116) patients (Fig. [124]4a). A two-way ANOVA analysis was
used, and 282 and 83 metabolites were identified as in relation to nCRT
dosage and pCR status, respectively (Fig. [125]4b and Supplementary
Data [126]5). Notably, two metabolites in phenylalanine metabolism,
3-phenylpropanoic acid (3-PPA) and phenylacetylglutamine (PAGln), were
found to have the most prominent changes between pCR and non-pCR
patients (Fig. [127]4c). Phenylalanine is first metabolized to
phenylpyruvic acid by aromatic amino transferase, then converted to
3-PPA and PAGln with presence of gut microbiota^[128]33,[129]34
(Fig. [130]4d and Supplementary Fig. [131]5). Our analyses showed that
3-PPA was significantly higher in non-pCR patients at all time points
(Fig. [132]4e). PAGln also showed a higher level in non-pCR patients,
but at baseline and after 15th nCRT only. There was also a trend of
difference between patients with pCR and non-pCR after 5th nCRT
(P = 0.074) and after 25th nCRT (P = 0.096). The level of 3-PPA was
decreased gradually with nCRT dosages in non-pCR patients. In contrast,
no further reduction of PAGln was observed in pCR patients after the
15th nCRT. Before surgery, the significant difference in 3-PPA was
still kept between pCR and non-pCR patients (Supplementary
Fig. [133]6a). We checked the metabolomics data from another
independent cohort from one of our previous publications^[134]35, and
verified that pheylacetylglutamine (PAGIn) is also closely associated
with the status of colon rectal cancer. The results demonstrated that
levels of PAGIn were significantly higher in plasma samples of CRC
patients compared to those in polyp controls. Similarly, for CRC
patients, levels of PAGIn were also significantly deceased after tumor
removal surgery (Supplementary Fig. [135]7).
Fig. 4. Phenylalanine metabolism underlies distinct therapeutic responses of
nCRT.
[136]Fig. 4
[137]Open in a new tab
a Different therapeutic strategies for pCR and non-pCR patients. b
Differentially changed metabolites related to nCRT dosage, pCR status,
and both combined using the two-way ANOVA analysis (P < 0.05). c
3-Phenylpropanoic acid and phenylacetylglutamine are the most
significant metabolites related to nCRT dosage and pCR status. The red
dash line represents the cut-off of P value 0.05 (Two-way ANOVA test).
d The scheme for the metabolic pathway of 3-phenylpropanoic acid and
phenylacetylglutamine. e Relative abundances of 3-phenylpropanoic acid
and phenylacetylglutamine in response to nCRT dosage (At individual
time points, n = 38, 35, 37, 38 for pCR and n = 116, 96, 113, 116 for
non-pCR). Two-sided Wilcoxon test. The dot depicts the mean values.
Error bars represent 95% confidence intervals. f Levels of
3-phenylpropanoic acid and phenylacetylglutamine between pCR and
non-pCR patients in different UGT1A1 genotypes (*1*1: pCR, n = 21,
non-pCR, n = 73; *1*28: pCR, n = 12, non-pCR, n = 18). Two-sided
Wilcoxon test; ns, not significant. The centerline of boxes depicts the
median values; the bottom and top box edges correspond to the first and
third quartiles, and whiskers indicated the minimum and maximum values.
We also checked the association between the two metabolites and the
toxicity of the treatment and the hematologic toxicities of the
patients at baseline. Pre-therapeutic serum levels of the two
metabolites between patients with diarrhea and without diarrhea showed
no differences in 3-PPA and PAGln between the two groups (Supplementary
Fig. [138]8a, b). Correlation analysis showed that 3-PPA had a weak
negative association with white blood cells (WBC) (P < 0.05, r = −0.18)
and PAGln also presented a weak negative association with the lowest
level of hemoglobin (Hb) (P < 0.05, r = −0.18; Supplementary
Fig. [139]8c).
To address the association between gut bacteria involved in
phenylalanine metabolism and therapeutic responses of nCRT, we further
analyzed the microbiome data at baseline in the previous independent
cohort in which none of the patients overlapped with our study^[140]36.
According to the drug treatment, pCR patients (n = 26) and non-pCR
patients (n = 46) treated with the combination of capecitabine with
irinotecan were selected for analysis using the Linear Discriminant
Analysis (LDA). As the results showed in Supplementary Fig. [141]9, 14
taxa were significantly different between pCR and non-PCR patients
(P < 0.05, LDA score > 2). In particular, families in Clostridiales
were showed to have lower levels in pCR patients compared with that in
non-pCR patients. For example, Clostridium IV, which has been reported
to have a positive association of 3-phenylpropanoic acid (3-PPA)
production^[142]37, was found to have a significant lower level in pCR
patients compared with that in non-pCR patients (Supplementary
Fig. [143]9c). Given that the read for species Clostridium sporogenes
was not detected, we analyzed the levels of genus and identified a
notable trend of lower levels of Clostridium sensu stricto in pCR
patients compared with those in non-pCR patients albeit that the P
value was calculated as 0.07 (Supplementary Fig. [144]9d). Overall, we
showed that in an independent cohort, gut microbiota that associated
with aromatic amino acid metabolism were linked to the pCR status of
patients treated with nCRT for colorectal cancer.
Our previous phase III clinical trial has demonstrated that nCRT guided
by UGT1A1 genotyping significantly improved the pCR rate of LARC
patients from 15% to 30%^[145]27. Therefore, we examined the
pre-therapeutic differences in 3-PPA and PAGln between pCR and non-pCR
patients on the basis of UGT1A1 genotyping. Interestingly, patients
with UGT1A1*1*1 genotype showed significantly higher levels of 3-PPA
and PAGln in non-pCR group than those in pCR group in baseline serum
samples prior to nCRT (Fig. [146]4f). However, elevations of two
metabolites were not observed in patients with UGT1A1*1*28 genotype.
The significant difference in 3-PPA was still maintained between pCR
and non-pCR groups in patients with UGT1A1*1*1 genotype after 15^th
nCRT (Supplementary Fig. [147]6b). Therefore, the results suggest that
both UGT1A1 genotype status and serum metabolite levels contribute to
the therapeutic responses of nCRT. For patients with UGT1A1*1*1
genotype, the lower serum levels of 3-PPA and PAGln indicate a higher
chance for pCR. The genetic and metabolic factors are interplayed and
closely associated with the nCRT efficacy. These findings highlighted
the importance of phenylalanine metabolism underlying the therapeutic
responses of the UGT1A1 genotyping-based nCRT.
Elevated essential amino acids are beneficial to pCR in nCRT
In the two-way ANOVA analysis, we also found that 33 metabolites had
significant interactive effects on both neoadjuvant chemoradiotherapy
(nCRT) dosage and pathological complete response (pCR) status (within
red circle; Fig. [148]4b). Pathway enrichment analysis of these
metabolites showed that the amino acid related pathways were
significantly enriched (Fig. [149]5a). We next investigated the
metabolic coordination between amino acids before and after the nCRT
treatment (Times 1 and 4) in pCR and non-pCR patients. Pearson
correlation analyses identified 15 positively correlated amino acids in
pCR patients, with up to 11 of them being present for essential amino
acids. Nevertheless, only 7 positive correlations were found in non-pCR
patients (Fig. [150]5b, Supplementary Fig. [151]10, and Supplementary
Data [152]6). Closer examinations of the total, essential, and
non-essential amino acids showed no differences between pCR and non-pCR
patients at baseline prior to nCRT (Fig. [153]5c). Strikingly, after
the completion of nCRT (Time 4, after the 25th nCRT), significantly
higher levels of essential and total amino acid levels in pCR patients
than that in non-pCR patients were observed. As a comparison, no
differences were found in the levels of non-essential amino acids
(Fig. [154]5d). Comparative analyses of individual amino acids showed
that pCR patients had higher levels of major essential amino acids
including leucine, isoleucine, methionine, threonine, and tryptophan
that those in non-pCR patients (Fig. [155]5e). For non-essential amino
acids, only proline showed significant difference between pCR and
non-pCR patients. Next, we constructed a logistic regression model and
calculated the odds ratio for each amino acid, and investigated the
contribution of amino acid to pCR status (Fig. [156]5f). The results
showed that asparagine, proline, serine, threonine, methionine,
leucine, and histidine had odds ratios higher than 1, suggesting that
higher levels of these amino acids are beneficial to pathological
complete response with nCRT treatment. Collectively, the results
demonstrated that amino acids are significant metabolic traits to
reflect both nCRT dosage and pCR status for LARC patients. In
particular, essential amino acids had no differences at baseline prior
to nCRT but were significantly elevated in the serum samples of pCR
patients after the completion of nCRT. The data also suggests that diet
and nutrition intervention especially supplementation of essential
amino acids during nCRT may have a beneficial contribution to tumor
remission for LARC patients.
Fig. 5. Elevated essential amino acids are beneficial to pCR in nCRT.
[157]Fig. 5
[158]Open in a new tab
a Pathway enrichment analysis of metabolites related to both nCRT
dosage and pCR status (Hypergeometric test; P < 0.05). b Metabolic
correlation between two amino acids in pCR and non-pCR patients
(Pearson correlation; r > 0.60 and two-sided Student’s t test
P < 0.05). c, d Levels of amino acids in serum samples of pCR (n = 38)
and non-pCR (n = 116) patients before and after nCRT: c, before nCRT at
baseline; d, after the 25th nCRT (Two-sided Wilcoxon test; ns, not
significant). e Levels of amino acids in serum samples between pCR
(n = 38) and non-pCR (n = 116) patients after nCRT. The data was
normalized to the levels in baseline (Two-sided Wilcoxon test). The
centerline of boxes depicts the median values; the bottom and top box
edges correspond to the first and third quartiles, and whiskers
indicated the minimum and maximum values. f Odd ratios of amino acids
after nCRT related to the pCR status (two-sided z-test of logistic
regression; ns, not significant). pCR (n = 38) and non-pCR (n = 116).
Error bars represent 95% confidence intervals.
Discussion
In this study, we characterized the serum metabolic profiles of locally
advanced rectal cancer patients with neoadjuvant chemoradiotherapy and
identified responsive metabolic traits associated with the treatment
course, toxicities, and therapeutic responses. Our initial analyses
comparing five time points revealed marked differences in metabolic
responses over the course of nCRT. Amino acid metabolism pathways
including valine leucine and isoleucine biosynthesis, arginine
biosynthesis, and glycine serine and threonine metabolism were
significantly affected in response to nCRT treatment. We found that
metabolites in tryptophan metabolism such as tryptophan, kynurenic
acid, indole-3-acetic acid, and indole-3-lactic acid were exclusively
decreased at the 25th nCRT (Supplementary Fig. [159]2). A previous
study by Guo et al. showed that two tryptophan metabolites, kynurenic
acid and indole-3-carboxaldehyde, have a long-term radioprotective
function in a mouse model^[160]23. Wang et al. also reported the
up-regulation of tryptophan metabolism as a result of
irinotecan-induced intestinal damage in mouse gut^[161]24. On the other
hand, cancer cells have high levels of tryptophan metabolites to
enhance tumor malignancy through aryl hydrogen receptor (AHR)
activation^[162]38. Given its roles in both regulating tumor growth and
inducing chemoradiotherapeutic effects, the decreased tryptophan
metabolites discovered in our LARC patient cohort may account for this
interrelation during nCRT.
Chemoradiotherapy is often accompanied with adverse effects including
diarrhea, leukopenia, and neutropenia, which contribute to unfavorable
clinical outcomes of nCRT treatment^[163]12–[164]14. Although studies
have indicated the associations between endogenous metabolites and
diarrhea^[165]24,[166]39, our results found that the pre-therapeutic
metabolic traits are considerably different between patients with and
without diarrhea. More importantly, we reported a predictive model
using the pre-therapeutic serum metabolites for diarrhea prediction in
the context of nCRT. The combination of serine, uridine, phenylalanine
and patient sex enabled differentiation between patients with and
without diarrhea. We also established the relationships between
metabolite levels at baseline and important hematologic toxicities,
such as WBC, NEUT, Hb, and BPC values. We found that WBC, NEUT, and Hb
correlated metabolites were enriched in TCA and aminoacyl-tRNA
biosynthesis pathways, while BPC correlated metabolites were highly
enriched in carbohydrate metabolism. These results highlighted the
potential of serum metabolomic traits prior to therapy for identifying
individuals who are at high risk of nCRT toxicity, which could enable
individualized treatment for LARC patients.
A notable finding of this study is the sex-specific difference in
carnitine metabolism that is linked to diarrhea induced by nCRT.
Notably, a growing number of independent clinical trial studies also
demonstrated that sex is a significant factor responsible for the
disparity of adjuvant treatment-induced toxicity for CRC, with females
being consistently at higher risk of diarrhea^[167]15,[168]40–[169]43.
This agreement of sex factor identification between our prediction
models and other independent studies strengthens the presented results
to a certain extent. In our study, we revealed that female patients
with diarrhea had significant lower levels of serum carnitines than
that in male patients with diarrhea. Acyl carnitines are indispensable
metabolite hubs for β-oxidation of fatty acids, which supply source of
energy for cell growth and proliferation^[170]44. Sex hormones
including estradiol and estrogen were showed to inhibit β-oxidation
through action on carnitine palmitoyltransferase^[171]45,[172]46.
Although high dose of L-carnitine administration has been reported to
cause diarrhea in healthy individuals^[173]47, it is unclear the
intertwined links between carnitine metabolism mediated by sex hormones
and the diarrhea induced during chemoradiotherapy. Our results suggest
another potential nutrient intervention avenue for female patients with
diarrhea in the context of nCRT, and further experimental and clinical
studies are warranted.
With treatment of nCRT, only 10–35% of LARC patients can achieve
pathological complete response. Since the status of pathological
complete response cannot be confirmed before surgery, presurgical
examination of pCR with high accuracy is invaluable in guiding the
selection of patients appropriate for the watch-and-wait
strategy^[174]7. We found that pCR patients had significantly lower
levels of 3-phenylpropanoic acid (3-PPA) and phenylacetylglutamine
(PAGln) than non-pCR patients. It has been reported that
phenylacetylglutamine was significantly increased in serum and urine of
CRC patients^[175]48,[176]49, indicating that PAGln is possibly linked
to colorectal cancer progression. 3-phenylpropanoic acid is metabolized
through reductive pathway of phenylalanine metabolism by the gut
symbiont Clostridium sporogenes^[177]50. A previous study demonstrated
that genetic modulation of Clostridium sporogenes metabolic products,
aromatic amino acids, alters host intestinal permeability and immune
activation^[178]33. In addition, nCRT responders and non-responders
differ remarkably in gut microbiome components^[179]36. A previous
study by Colosimo et al. showed that bacterial derived 3-PPA can
interact with G protein-coupled receptors (GPCRs) associated with
diverse functions within the nervous and immune systems, among
others^[180]51. Nemet et al. also reported that PAGln can mediate
cellular responses via GPCRs and act via adrenergic receptors
(ADRs)^[181]52. Though there are limited functional studies on 3-PPA
and PAGln, it has been suggested those aromatic amino acids participate
in the interplay between gut microbes and host immune response through
interactions with key signaling receptors such as GPCRs. GPCRs are
signaling receptors that function in many cancers by regulating
cellular proliferation, invasion, migration, immune cell-mediated
functions, angiogenesis and survival at metastatic sites^[182]53.
Proliferation of many cancers is stimulated by GPCR agonists^[183]54.
Therefore, we speculate that the interplay between the two metabolite
abundances and the signaling pathways is a potential determinant for
response to nCRT treatment. Our present study showed that the 3-PPA and
PAGln, which were demonstrated as potential GPCR agonists, are risk
factors for failure of achieving pathological complete response. The
detailed mechanism of those metabolites may be related to the complex
signaling affected by cancer and chemoradiation, which are strongly
dependent on dose and individual heterogeneity, which requires further
biological validation. Though the levels of phenylalanine in serum
showed no significant differences between pCR and non-pCR, the two
products of phenylalanine metabolism, 3-PPA and PAGln, which has been
proved associated with gut microbiome^[184]33,[185]55, showed
significant differences between pCR and non-pCR patients, suggesting
that the gut bacteria involved the different responses of nCRT therapy.
Thus, we reasoned that gut bacterial metabolites, in particular the
intermediates of phenylalanine metabolism, have potential in predicting
therapeutic responses for LARC patients with nCRT treatment. Beyond
that, we found that the differences in 3-phenylpropanoic acid and
phenylacetylglutamine between pCR and non-pCR patients possessed
specificity in UGT1A1 genotype, wherein differences were seen in
patients with UGT1A1*1*1 genotype but not in patients with UGT1A1*1*28
genotype. In irinotecan plus capecitabine-based nCRT, UGT1A1 is
responsible for inactivation of the SN-38, which is a metabolic product
of irinotecan^[186]16. Given that the maximum tolerated dose of
irinotecan decreases with an increase in the number of defective UGT1A1
alleles, patients with UGT1A1*1*28 genotype are prone to have higher
chance of the nCRT induced toxicities^[187]27,[188]56. Therefore, it
seems plausible that differences in 3-phenylpropanoic acid and
phenylacetylglutamine seen in patients with UGT1A1*1*1 genotype could
account for toxicity tolerance between pCR and non-pCR patients during
the course of nCRT. This is also indicated by the result that pCR
patients have enhanced essential amino acid metabolism. These results
suggest that diet and nutrition intervention with supplementation of
essential amino acids may have a positive contribution to improve
responses to nCRT for non-pCR patients, but more clinical trials are
required before this intervention can be implemented in clinical
practice.
Previous studies have demonstrated the application potential of omics
analysis for nCRT treatment in rectal and other
cancers^[189]25,[190]26,[191]36,[192]57–[193]60 (Supplementary
Table [194]8). For example, Rodriguez et al. identified that patients
with pCR had lower level of valine at baseline and those with relapse
had lower level of succinate by GC-MS based targeted
metabolomics^[195]26. Diaz et al. performed metabolomics analysis in
breast cancer patients with nCRT and found that glycohyocholic acid and
glycodeoxycholic acid can classify triple-negative patients regarding
treatment response^[196]59. Wang et al. identified numerous
differentially expressed genes and miRNAs from microarray datasets of
nCRT responder group in patients with esophageal squamous cell
carcinoma^[197]60. However, the longitudinal analysis for the whole
processing of nCRT is rare in study design and sample size of previous
studies is small. Our previous work also identified 15 potential
metabolite biomarkers to predict tumor response to neoadjuvant
chemo-radiation therapy at baseline in patients with locally advanced
rectal cancer^[198]25. Compared to previous studies, the present study
is conceptually and technically different in terms of the following
aspects: (1) Based on our results of a recent phase III study
(CliClare), we enrolled patients (n = 165) with treatment of both
capecitabine and irinotecan in the present study. (2) The cohort in the
current study is a longitudinal study designed for longitudinal
analyses, which can provide valuable insights into the dynamic
metabolic traits in response to nCRT in patients with LARC. (3) The
clinical endpoint in this study is the presence of pathological
complete response (pCR), which is optimal and more stringent for
clinical assessment than tumor regression grade (TRG) that was used in
the previous study.
There are some limitations in the present study. The purpose of this
study was not to develop a clinical test for prediction of nCRT
responses, for which absolute concentrations of metabolites would be
required, but to identify potential metabolites that could be
associated with nCRT treatment course, toxicities, and therapeutic
responses. Therefore, a targeted quantitative measurement method was
not used for our study. However, it was feasible to use the peak areas
of metabolite ions to compare levels of these metabolites and correlate
them with the nCRT treatment. Although the sample size of our study is
larger than many previous metabolomics studies on chemo- and
radio-therapies, the small sample size is still a potential limitation
for this study and further analysis in a larger cohort is required. We
also hope to recruit an additional cohort of patients with samples from
multiple sites for validation in the near future. To partially validate
our results, we first analyzed another metabolomics study for
colorectal cancer patients, which revealed that the PAGln was also
linked to colorectal cancer progression (Supplementary Fig. [199]7).
Most importantly, for CRC patients, levels of PAGIn were significantly
deceased after tumor removal surgery. In addition, results from gut
microbiota validated our findings that aromatic amino acid metabolism
was correlated with the pCR status for LARC patients treated with nCRT
(Supplementary Fig. [200]9). Besides, pathway enrichment analysis in
this study was based on differential metabolites in the circulating
metabolome, where actually pathways do not exist in contrast to tissue
or cellular metabolomes. We do think the ideal experiment design should
include tissue samples to identify metabolic pathways that are
originally impacted by the nCRT treatment. However, it is challenging
to obtain tissue samples from the patients over the treatment course of
nCRT. Given that the tumor-originating metabolites enter the
circulatory system, we speculate that the significantly changed
metabolites detected in blood could reflect changes pertaining to
tissue or cellular metabolome. In our previous publication, we analyzed
dysregulated metabolites from paired tissue and plasma samples from the
same CRC patients, and demonstrated that tissue-correlated metabolites
in plasma accurately reflected the pathological status and tumor stages
of CRC patients and have a high diagnostic potential for clinical
applications^[201]35. In addition, metabolites produced by certain
bacteria and dietary metabolism can enter the circulatory system. For
example, glyoxylate and dicarboxylate metabolism was enriched because
of the dysregulated changes in serine and citrate. Although there is an
ongoing debate what pathway analysis is suitable to analyse the
circulating metabolome, many studies have used the pathway enrichment
analysis on the basis of circulating metabolome and revealed valuable
insights into human health and diseases^[202]61,[203]62. Though not the
prominent focus in the present study, we believe that the pathway
analysis helps to interpret results in light of the whole-body
metabolism which has been impacted by nCRT. Taken together, our primary
results revealed therapeutic toxicities and responses of nCRT and
showed potential benefits for LARC patients.
Methods
Sample collection
The study protocol was approved by the central ethics committee of
Fudan University Shanghai Cancer Center (Shanghai, China). The serum
samples (n = 743) of 165 LARC patients were collected from Fudan
University Shanghai Cancer Center between July 2014 and January 2018.
Patients were recruited in an experiment group of a multicenter,
randomized, open-label phase III clinical trial in China (CinClare,
ClinicalTrials.gov identifier: [204]NCT02605265). Details of the cohort
have been provided in the clinical report^[205]27. In brief, eligible
patients were aged 18–75 years old and diagnosed with clinical stage
T3-4 and/or N + rectal adenocarcinoma. The inclusion criteria included
a Karnofsky performance status score ≥70, a UGT1A1 genotype of *1*1 or
*1*28, adequate bone marrow function (a hemoglobin level ≥9 g/dL,
neutrophil count ≥ 1500/mL, and platelet count ≥100,000/mL), liver
function (total bilirubin level <1.5 times the upper limit of normal;
albumin level >30 g/L; and aspartate aminotransferase, alanine
aminotransferase, and alkaline phosphatase levels <2.5 times the upper
limit of normal), and normal kidney function (creatinine concentration
below the upper limit of normal). The sex information of patients
enrolled in this study was determined by self-reporting. Detailed
information on sex is provided in Supplementary Table [206]1. All
enrolled patients have provided informed consent. Patients were not
compensated for their participation in the trial, but were provided
with treatment free of charge. Patients received the complete treatment
course of nCRT (50 Gy/25 fractions; concurrent capecitabine +
irinotecan chemotherapy) and 1 cycle of interval chemotherapy (CAPIRI,
capecitabine+ irinotecan). The dosage of irinotecan was determined by
UGT1A1 genotype. For patients with the UGT1A1*1*1 genotype and
UGT1A1*1*28 genotype, the weekly doses of irinotecan were administered
at 80 mg/m^2 and 65 mg/m^2, respectively. In this study, diarrhea
diagnosis was determined by clinical symptoms according to CTCAE
(Common terminology criteria for adverse events, version 4.0). In
brief, patients with an increase of <4 stools per day over baseline and
mild increase in ostomy output compared to baseline were diagnosed as
grade 1 diarrhea; patients with an increase of 4–6 stools per day over
baseline and moderate increase in ostomy output compared to baseline
were diagnosed as grade 2 diarrhea; patients with an increase of ≥7
stools per day over baseline, incontinence, hospitalization indicated
and severe increase in ostomy output compared to baseline were
diagnosed as grade 3 diarrhea; patients with diarrhea induced
life-threatening consequences and even death were diagnosed as grade 4
and grade 5 diarrhea. Sex was also considered in analysis as it is
known that females had higher incidence of the nCRT induced diarrhea
than males. In this study, no grade 4 or grade 5 diarrhea occurred
during nCRT. In this study, pCR was defined as pathological T0N0M0 and
all pCR statuses were evaluated by two independent pathologists. If
their conclusions were inconsistent, it was evaluated again by a third
pathologist. Serum samples for each patient were collected at the
following time points: before nCRT (Time 1), at the 5th fractions of
nCRT (Time 2), at the 15th fractions of nCRT (Time 3), at the 25th
fractions of nCRT (Time 4), and after the rest for two months and
within 2 days before surgery (Time 5) (Supplementary Fig. [207]1). For
serum collection, all participants were in an overnight fasting state,
and 5 mL of peripheral venous blood was drawn in the morning.
Reagents and sample preparation
LC−MS grade water (H[2]O) and methanol (MeOH) were purchased from
Honeywell (Muskegon, USA). Ammonium hydroxide (NH[4]OH) and ammonium
acetate (NH[4]OAc) were purchased from Sigma-Aldrich (St. Louis, USA).
Chemical standards of metabolites were purchased from J&K (Beijing,
China), Sigma (St. Louis, USA), Carbosynth (Berkshire, UK), TCI (Tokyo,
Japan), and Energy Chemical (Shanghai, China). Serum samples (50 µL)
were extracted using 150 μL MeOH with internal standards (d3-leucine
and d8-phenylalaine). The samples were then vortexed for 30 s and
sonicated for 15 min. To precipitate proteins, the samples were
incubated for 1 h at −20 °C, followed by 15 min centrifugation at
17,500×g and 4 °C. The supernatants were transferred to HPLC vials and
stored at −80 °C prior to LC−MS analysis.
LC−MS analysis
The LC−MS analysis protocol followed our previous publication^[208]63.
The data acquisition was performed using a Vanquish UHPLC coupled to a
Orbitrap Exploris 480 (ThermoFisher Scientific, United States). The raw
data was acquired using Xcalibur (version 4.4.16.14). A Waters ACQUITY
UPLC BEH amide column (particle size, 1.7 μm; 100 mm (length) × 2.1 mm
(i.d.)) and UPLC HSS T3 column (1.8 μm; 100 mm (length) × 2.1 mm
(i.d.)) was used for the LC separation and the column temperature was
kept at 25 °C. For amide column, mobile phase A was water with 25 mM
ammonium hydroxide (NH[4]OH) and 25 mM ammonium acetate (NH[4]OAc), and
B was ACN for both the positive (ESI+) and negative (ESI−) modes. The
flow rate was 0.5 mL/min and the gradient was set as follows:
0–0.5 min, 95% B; 0.5–7 min, 95% B to 65% B; 7–8 min, 65% B to 40% B;
8–9 min, 40% B; 9–9.1 min, 40% B to 95% B; 9.1–12 min, 95% B. The
injection volume was 2 μL. For T3 column, mobile phase A was water with
0.1% formic acid, and B was ACN with 0.1% formic acid for both the
positive (ESI+) and negative (ESI−) modes. The flow rate was 0.5 mL/min
and the gradient was set as follows: 0–8 min: 1% B to 99%B; 8–10 min:
99% B; 10–10.1 min, 99% B to 1% B; 10.1–12 min: 1% B; The injection
volume was 2 μL. All the samples were randomly analyzed during data
acquisition. The QC sample was prepared by pooling aliquots of all
subject samples and injected every 20 samples.
The data acquisition was operated in full MS scan mode and dd-MS2 scan
mode. The source parameters were set as follows: spray voltage, 3500 V
or −2800 V for positive or negative mode, respectively; aux gas heater
temperature, 350 °C; sheath gas, 50 arb; aux gas, 15 arb; capillary
temperature, 400 °C. The resolution for full MS scan mode was set as
60,000 and AGC target was set as 1e6 for both positive and negative
modes. Maximum IT was set as 100 ms. Mass range was set as 70–1200 Da.
For the dd-MS2 scan mode, MS resolution was set as 30,000 and AGC
target was set at 1e5. Maximum IT was set as 60 ms. The Top N setting
was set as 6. Isolation width was set as 1.0. The collision energy was
set as SNCE 20-30-40%. The dynamic exclusion was set as 3.0 s and
isotope exclusion was on.
Metabolomics data processing
The metabolomics data processing protocol followed our previous
publication^[209]63. ProteoWizard (version 3.0.20360)^[210]64 was used
to convert raw MS data (.raw) files to the mzXML format, and R package
“XCMS” (version 3.12)^[211]65 was used for peak detection, retention
time correction, and peak alignment. The XCMS processing parameters
were set as follows: mass accuracy for peak detection = 10 ppm; peak
width c = (5, 30); snthresh = 3; minfrac = 0.5. For each metabolic
feature, the intensity more than 5 SD were considered as outlier and
set as a missing value. The features with more than 70% of missing
values in QC samples was removed. The remaining missing values were
imputed using the k-Nearest Neighbor (KNN) algorithm^[212]66. The
generated peak table was uploaded to MetFlow
([213]http://metflow.zhulab.cn/)^[214]67 for normalization and
integration to remove unwanted systematic errors based on QC samples.
Metabolic peaks with RSDs less than 30% in QC samples were used for
subsequent analysis. Metabolite identification was performed using our
previously published software MetDNA
([215]http://metdna.zhulab.cn/)^[216]63. In brief, we used an in-house
metabolite spectral library for metabolite annotation by matching
accurate mass, retention time and MS/MS similarity. The matched
metabolites were considered as level 1 identification according to
MSI^[217]28. Metabolite identifications with MSI level 2 confidence
were achieved by matching accurate mass and MS/MS similarity. External
public metabolite library and lipid spectral library were used. The
rest of the metabolite identifications annotated from MetDNA were
considered as MSI level 3. The MS/MS spectral similarity was calculated
using the dot-product algorithm and the cutoff was set as 0.8. All
metabolite and lipid identifications were provided in Supplementary
Data [218]1. Two internal standards were spiked into individual samples
(d3-leucine and d8-phenylalanine) to monitor the reproducibility during
the LC−MS data acquisition, with the relative standard deviations
(RSDs) of peak areas calculated as 3.2% and 4.7%, respectively
(Supplementary Fig. [219]11a). After data normalization, the median
RSDs of metabolites measured in HILIC − MS and RPLC−MS were 11.4% and
12.0% (Supplementary Fig. [220]11b), respectively. Principal components
analysis (PCA) was conducted to assess the reproducibility of QC
samples (Supplementary Fig. [221]11c). QC samples clustered tightly in
PCA plot for both HILIC and RPLC modes. Those results indicated the
excellent reproducibility and good data quality.
Microbiome data processing
The gut microbiome dataset was from an independent cohort which was
previous published^[222]36. The processed OTU abundance table and
taxonomic annotation of the study was used for further validation.
Paired-end raw sequences were merged using FLASH (version 1.2.8) and
clean sequences were obtained after quality checking with fqtrim
(version 0.9.4). The removal of chimeras, generation of representative
sequences and operation taxonomy units (OTU) feature table were
completed by Vsearch (version 2.11.1). The representative sequences
were aligned to the Ribosomal Database Project classifier for taxonomic
annotation. Only patients received combination therapy of irinotecan
and capecitabine was selected. Among them, 46 patients were non-pCR
while 26 were pCR. The features without more than 3 counts in 10% of
samples were removed. The data was normalized by total sum scaling
(TSS). The Linear Discriminant Analysis (LDA) effect size (LEfSe) were
performed by “MASS” (v7.3.54) package of R. The R packages “phyloseq”
(version 1.38.0) and “ggtree” (version 3.2.1) were used for
visualization.
Statistical analyses
The statistical analyses were performed using R (version 3.6.1). The
SAM (Significance Analysis of Microarrays)^[223]29 was performed using
an R package “samr” to identify the metabolites altered during nCRT
treatment course. The distribution-independent ranking tests (based on
the Wilcoxon test) and the sample-wise permutation were used to
ascertain significance (false discovery rate, FDR < 0.05).
The pathway enrichment analysis was performed using MetaboAnalyst
([224]https://www.metaboanalyst.ca/)^[225]68 embedded with the Kyoto
Encyclopedia of Genes and Genomes (KEGG) pathway database^[226]69
(accessed on October, 2019). Hypergeometric test was used to calculate
P value and out-degree centrality for topology analysis. All compounds
in the selected pathway library was used for reference metabolome.
Though we selected homo sapiens as the species for database search,
some enriched pathways were mixed with mammalian and non-mammalian
pathways, due to a long-standing and unresolved pathway curation issue
in KEGG database. The detailed information of enriched metabolic
pathways was provided in Supplementary Data [227]7.
The R package “pheatmap” (version 1.0.12) was used for hierarchical
clustering analysis (HCA) and the median level was used to represent
the level of all patients. The R package “glmnet” (version 4.0.2) was
used for feature selection by the adaptive lasso^[228]70. At first, all
metabolites and clinical covariates (age, sex, and BMI) were scaled and
used in the selection of predictors. The adaptive weights vector ω is
calculated as:
[MATH: ω=1βini<
/mi>γ<
/mi> :MATH]
1
Where β^ini is the initial estimate of the coefficients obtained
through ridge regression with a 10-fold cross-validation. γ is a
positive constant for adjustment of the adaptive weights vector. In
this study, it was set as 1. Subsequently, the adaptive weights vector
ω was applied in the lasso regression as penalty factor. The optimal
lambda was chosen by a 10-fold cross-validation. After the feature
selection, the logistic regression was used to build the model for
predicting the chance of diarrhea using serum metabolites at the
baseline. Additionally, to consider the limited sample size, we
restricted the number of predictors no more than four in the final
model. The logistic model using four predictors to predict the chance
of diarrhea is finally expressed as:
[MATH: RiskScore=eLog<
mi>itP1+eLog<
mi>itP
:MATH]
2
[MATH: LogitP=−0.14<
/mn>−1.07×MUridine
−1.30×MSerine
+0.98×MPheny<
mi>lalaine+1.17×Sex<
/mi> :MATH]
3
The logistic model was tested by bootstrapping using the R package
“bool” (Supplementary Fig. [229]12). In brief, 63% randomly selected
patients from the dataset were selected as discovery data to build the
prediction model, and the remaining 37% patients were used as
validation data. This random sampling with model construction and
validation procedure was repeated 1000 times. Powers of predictors in
diarrhea prediction model (Supplementary Table [230]3) were calculated
by the G*Power software (version 3.1.9.4) and a z-test with two tails
specific for the logistic regression was used.
The similar feature selection method was used in the predictions of
hematologic toxicity. In this prediction, the multiple linear
regression was used. The R package “pROC” was used to plot receiver
operating characteristic (ROC) curves and calculate the area under the
curve (AUC) value and 95% confidence interval (CI). Two-way ANOVA
analysis was performed by the R function “aov”. The metabolites with
p < 0.05 in dosage, pCR, and interaction dimensions were selected
independently. The nonparametric Wilcoxon rank-sum test was used to
compare the differences in metabolite levels between pCR and non-pCR
patients. To calculate the correlation between amino acids, Pearson
correlation was performed using the R package “Hmisc”.
Reporting summary
Further information on research design is available in the [231]Nature
Portfolio Reporting Summary linked to this article.
Supplementary information
[232]Supplementary Information^ (2.7MB, pdf)
[233]41467_2022_35511_MOESM2_ESM.docx^ (15.7KB, docx)
Description of Additional Supplementary Files
[234]Supplementary Data 1-7^ (5.5MB, zip)
[235]Reporting Summary^ (1.5MB, pdf)
Acknowledgements