Abstract
Driver mutations and chromosomal aneuploidy are major determinants of
tumorigenesis that exhibit complex relationships. Here, we identify
associations between driver mutations and chromosomal aberrations that
define two tumor clusters, with distinct regimes of tumor evolution
underpinned by unique sets of mutations in different components of DNA
damage response. Gastrointestinal and endometrial tumors comprise a
separate cluster for which chromosomal-arm aneuploidy and driver
mutations are mutually exclusive. The landscape of driver mutations in
these tumors is dominated by mutations in DNA repair genes that are
further linked to microsatellite instability. The rest of the cancer
types show a positive association between driver mutations and
aneuploidy, and a characteristic set of mutations that involves
primarily genes for components of the apoptotic machinery. The distinct
sets of mutated genes derived here show substantial prognostic power
and suggest specific vulnerabilities of different cancers that might
have therapeutic potential.
Subject terms: Cancer genomics, Genome informatics
__________________________________________________________________
The interplay between driver mutations and aneuploidy during
tumorigenesis is largely unexplored. Here, the authors show two types
of associations, leading to different therapeutic vulnerabilities and
prognoses.
Introduction
Acquisition of genetic alterations is thought to drive the progression
of normal cells through hyperplastic and dysplastic stages to invasive
cancer and, ultimately, to metastatic disease. In recent years,
analysis of the increasingly abundant cancer genomics, transcriptomics
and proteomics data has substantially improved our understanding of
tumor development through the activation of oncogenes and inactivation
of tumor suppressors^[28]1–[29]3. In addition to driver mutations in
oncogenes and/or tumor suppressors, the majority of solid tumors
display widespread whole chromosome or chromosome arm imbalances (here
termed aneuploidy), as well as large deletions, inversions,
translocations, and other genetic abnormalities^[30]4. Despite the fact
that numerical and structural chromosome abnormalities are the most
pronounced, distinguishing characteristics of cancer genomes, the role
of arm and chromosome level aneuploidy in tumor development remains
poorly understood^[31]5–[32]7. In particular, the genes and pathways
that might be affected by aneuploidy remain largely unknown.
Several studies have investigated the relationships between different
genetic alterations in cancer and reported an inverse correlation
between the number of recurrent copy number alterations and the number
of somatic mutations^[33]8,[34]9. However, more recent work has
demonstrated that the correlation between aneuploidy and non-silent
somatic mutation rate is actually positive for the majority of tumors,
but for several cancer types, including gastrointestinal and
endometrial tumors, this correlation is significantly
negative^[35]10–[36]12. TP53 is the only gene for which the mutation
rate has been shown to positively correlate with the aneuploidy level
across tumor types, consistent with previous
findings^[37]8,[38]10,[39]13. For other genes, the pan-cancer
associations of mutation rates with aneuploidy have been found to be
largely negative and less significant^[40]8.
Here we perform a pan-cancer analysis of the interplay between
mutations, specifically in cancer driver genes and chromosomal-arm
level aneuploidy, and its consequences for clinical outcome. In similar
to the reported for mutation load and all aneuploidies^[41]10–[42]12,
we find positive correlations between chromosomal arm level aneuploidy
and driver mutations load for the majority of cancers, but in
gastrointestinal and endometrial tumors, the correlation is strongly
negative. The latter cancers also show an unexpected association of
driver mutations with improved overall survival rate. Identification of
unique mutational gene sets shows that, in the two clusters of tumors,
the load of driver mutations is associated with distinct DNA Damage
Response (DDR) pathways. In gastrointestinal and endometrial tumors,
high load of oncogenic mutations is predominantly observed in tumors
mutated in DNA repair genes, whereas in the other tumor types, high
load of oncogenic mutations corresponds to apparent inactivation of the
apoptosis network and DNA damage checkpoints. The ratio of the mutation
load in the DNA repair system to that in the checkpoint and apoptotic
machinery is shown to be a pan-cancer correlate of aneuploidy and
overall survival which subdivides tumors into two major classes. In the
first class, tumorigenesis appears to be driven, primarily, by
mutations in repair genes that allow mutations to accumulate
increasingly but preclude chromosomal aberrations. In the second class
of cancers, tumor development is apparently driven by mutations in DNA
damage checkpoint and apoptosis genes which allow uncontrolled cell
division accompanied by diverse chromosomal alterations. For the first
class that consists of gastrointestinal and endometrial tumors, we
additionally derive a mutational gene set that captures the mutual
exclusivity between aneuploidy and microsatellite instability (MSI).
This set reflects differences in therapeutic vulnerabilities and can be
used as an independent prognostic marker within this tumor class.
Overall, our analysis reveals genomic determinants of aneuploidy and
clinical outcome, uncovering their relations with driver mutations and
distinguishing DDR pathways that appear to promote tumor development
through separate courses.
Results
Associations of driver mutations, aneuploidy, and survival
For the purpose of this analysis, we integrated mutational, aneuploidy,
and clinical data from 8686 tumor samples from 32 solid tumor types
represented in The Cancer Genome Atlas (TCGA)^[43]11,[44]14
(Table [45]1, Supplementary Data [46]1). First, we analyzed the
correlation between the number of mutations^[47]15 in cancer driver
genes (which is used as a proxy for the number of actual driver
mutations) and aneuploidy levels in each tumor type. In agreement with
the previous observations for the overall mutational
load^[48]10,[49]11, the correlations were positive for most tumor
types, but significantly negative for gastrointestinal and endometrial
tumors in which we also noticed a higher load of driver mutations
(Fig. [50]1a). We next investigated the association between the number
of driver mutations and overall survival rates. We found that, although
in most tumor types, a large number of driver mutations is predictably
associated with poor outcome, most of the gastrointestinal and
endometrial tumors show an inverse relationship (Fig. [51]1a, b). This
trend is recapitulated with aggregated data from these two classes for
tumor types although the different survival rates in different tumor
types are likely to be a confounding factor in this analysis
(Fig. [52]1c). However, the overall mutational burden is positively
correlated with survival rates mostly in hypermutated tumors (including
those with a negative association between driver mutations and
survival, such as lung carcinomas; Supplementary Fig. [53]1),
consistent with previous findings^[54]16. Similar associations are
observed when using PolyPhen^[55]17 and SIFT^[56]18 scores to predict
functional alterations in driver mutations (Supplementary Fig. [57]2).
Furthermore, these associations are reproduced when controlling for the
total mutation burden and when considering whole chromosome aneuploidy
or separately analyzing arm gains and losses. Together, these
observations further support the unique associations characteristic of
gastrointestinal and endometrial tumors (Supplementary Figs. [58]3,
[59]4). We also examined the associations between focal Somatic Copy
Number Alteration (SCNA) levels and driver mutations for the 13 tumor
types with available focal SCNA data^[60]12 and found that these do not
necessarily fully conform with the pattern observed for whole
chromosome aneuploidy or arm gains and losses (Supplementary
Fig. [61]4). A more complete analysis of focal SCNA remains to be
performed. The unexpected, complex relationship between the load of
driver mutations, arm-level aneuploidy and patient survival partitions
tumors into two classes: one in which different types of genetic
alterations are positively correlated and appear to jointly account for
poor survival, and a second one where these events are observed in
distinct tumors, such that aneuploidy seems to be uniquely associated
with poor prognosis (Fig. [62]1d, Supplementary Fig. [63]5).
Table 1.
TCGA pan-cancer datasets.
Tumor type TCGA ID Number of cases Cases with MSI
Pancreatic adenocarcinoma PAAD 161
Sarcoma SARC 227
Cholangiocarcinoma CHOL 36
Lymphoid neoplasm diffuse large B-cell lymphoma DLBC 37
Prostate adenocarcinoma PRAD 469
Lung squamous cell carcinoma LUAD 495
Liver hepatocellular carcinoma LIHC 349
Lung adenocarcinoma LUSC 463
Testicular germ cell tumors TGCT 128
Bladder urothelial carcinoma BLCA 401
Adrenocortical carcinoma ACC 89
Kidney chromophobe KICH 65
Kidney renal clear cell carcinoma KIRC 344
Breast invasive carcinoma BRCA 757
Glioblastoma multiforme GBM 292
Skin cutaneous melanoma SKCM 456
Pheochromocytoma and paraganglioma PCPG 160
Thyroid carcinoma THCA 458
Ovarian serous cystadenocarcinoma OV 61
Thymoma THYM 105
Mesothelioma MESO 79
Cervical squamous cell carcinoma and endocervical adenocarcinoma CESC
278
Kidney renal papillary cell carcinoma KIRP 273
Head and neck squamous cell carcinoma HNSC 491
Brain lower grade glioma LGG 502
Esophageal carcinoma ESCA 162
Uveal melanoma UVM 84
Rectum adenocarcinoma READ 80 3
Uterine carcinosarcoma UCS 56 0
Stomach adenocarcinoma STAD 423 49
Colon adenocarcinoma COAD 278 53
Uterine corpus endometrial carcinoma UCEC 427 118
[64]Open in a new tab
Fig. 1. Pan-cancer association between the number of driver mutations, levels
of aneuploidy and overall survival.
[65]Fig. 1
[66]Open in a new tab
a Top panel: Boxplots showing the distribution of the number of driver
mutations per sample in each tumor type. Center lines indicate medians,
box edges represent the interquartile range, whiskers extend to the
most extreme data points not considered outliers, and the outliers are
plotted individually. Bottom panel: the corresponding correlation
coefficients between the number of driver mutations and aneuploidy
scores, and the hazard ratio values (log10 transformed) resulting from
Kaplan–Meier overall survival curves for samples with high vs. low
number of driver mutations (separated by the median). Positive
log10-hazard ratio values indicate that high load of driver mutations
is associated with worse survival, and negative log10-hazard ratio
values indicate that high load of drivers is associated with improved
survival. Statistical significance (log-rank and Spearman
rank-correlation P-value < 0.05) is indicated with asterisk. b The
hazard ratio values resulting from Kaplan–Meier overall survival
prediction curves for samples with high vs. low number of driver
mutations for different thresholds (y-axis), for different tumor types
(x-axis). The circle sizes represent the significance level measured as
log-rank P-value. c Kaplan–Meier curves predicting overall survival for
gastrointestinal and endometrial tumors (bottom panels) and for all
other tumors (top panels), for tumors with high vs. low number of
cancer driver mutations separated with two thresholds. The log-rank
P-values are indicated. d tumor clustering based on the associations
between aneuploidy and driver mutations (columns) for each tumor type
(rows). Source data are provided as a Source Data file.
Driver mutations and aneuploidy are genetic alterations that are
facilitated by genome instability, which is are a result of impairment
of DNA Damage Response (DDR^[67]19–[68]21). Consequently, we next
sought to identify the specific DDR pathways that are affected in the
two tumor classes, and to investigate how impairment of different forms
of DDR might promote tumor evolution through alternative routes.
Distinct DDR pathways characterize the two tumor classes
Starting with a set of 746 genes implicated in DNA damage response
(DDR) pathways (based on Gene Ontology annotations^[69]22,
Supplementary Data [70]2), we aimed to derive distinct mutational gene
sets of DDR genes that might be associated with the load of driver
mutations in each of the two tumor classes. To this end, we applied 100
repetitions of a genetic algorithm for every tumor type and calculated
a selection score for each DDR gene, along with the corresponding
binomial P-value (see Methods for details). Genes with a significant
combined P-value for one of the two classes of cancer types (Fisher
P-value < 0.1 for a single class) were selected, and two distinct
mutational gene sets of DDR genes were derived, each uniquely
associated with one of the two tumor classes (Fig. [71]2a). The
mutational gene set for the gastrointestinal and endometrial tumors
predominantly includes DNA repair genes (hereafter repair set), in
particular, base excision repair (XRCC1 and XRCC6), nucleotide excision
repair (NER, ERCC1-6), mismatch repair (MSH2-4 and MSH6, MLH1, and
MLH2), non-homologous end joining (PARP1 and BRCA1), and homologous
recombination (RAD51, XRCC2, and XRCC3). In contrast, the mutated gene
set for the second, larger tumor class encompasses numerous genes
involved in DNA damage checkpoints and damage-induced apoptosis
(hereafter, for brevity, apoptosis set, Supplementary Fig. [72]6),
primarily, TP53 and the associated apoptosis and checkpoint factors,
such as BCL3, BRCA2, CHEK2, PML, TOPORS, TP63, AEN, and SIRT1, which
are involved in the P53-dependent damage response. The mutation loads
of these sets show highly significant, positive correlations with the
loads of driver mutations across tumors in the respective class
(Fig. [73]2b, which is not observed for most of the randomly chosen
sets of mutation; Supplementary Fig. [74]7). The unique identities of
the two mutated gene sets are further corroborated by the observed
distinctive, highly significant enrichment of multiple DDR pathways
(according to GO) with genes from the respective sets (Fig. [75]2c,
Supplementary Fig. [76]6). Crucially, these sets show a pan-cancer
correlation with the clinical outcome, whereby a high ratio of
apoptosis to repair mutations strongly correlates with lower overall
survival (Fig. [77]2d), as well as lower progression-free interval and
disease-specific survival (Supplementary Fig. [78]8).
Fig. 2. Driver-associated mutation sets distinguish DNA repair from
damage-induced apoptosis.
[79]Fig. 2
[80]Open in a new tab
a Heatmaps showing selection P-values (negative log-scaled) assigned to
each gene in the selected mutational sets (rows) for every tumor type
and the combined P-values for each cluster of tumors (columns). b
scatter plots correlating the driver mutation load (y-axes) with the
repair set load (upper panel x-axis, for gastrointestinal and
endometrial tumors), and with the apoptosis set load (bottom panel
x-axis, for all other tumor types). c hyper-geometric enrichment
P-value for the DDR pathways enriched with genes from one of the
selected sets. d Kaplan–Meier curves predicting overall survival for
patients with higher repair set mutation rate (i.e. more repair set
mutations than apoptosis set mutations, blue) vs. those with higher
apoptosis set mutation rate (red), for gastrointestinal and endometrial
tumor samples (left panel) and all other tumor samples (right panel).
The two mutation sets show opposite associations with aneuploidy across
cancers: 30 of the 32 tumor types exhibit positive correlations between
aneuploidy and the apoptosis set mutations count, of which 13 were
significant (Spearman rank-correlation P-value < 0.05), whereas the
correlations between aneuploidy and the repair set mutation count were
negative for 25 cancer types, and significant for 5 of these
(Fig. [81]3a). The negative association of aneuploidy with the repair
mutation set is most pronounced in gastrointestinal and endometrial
tumors (right end of the spectrum in Fig. [82]3a), whereas the positive
association with the apoptosis set is mainly manifested at the left end
of the spectrum that includes tumors with lower loads of driver
mutations (Fig. [83]1a). The ratio of the mutation load in the
apoptosis set to that in the repair set positively correlates with
aneuploidy for nearly all tumor types (and significantly for 18 of
these, Fig. [84]3a), with the exception of brain lower grade glioma
(LGG). Thus, samples with a higher load in the repair set, as opposed
to the apoptosis set, show significantly elevated aneuploidy levels
across cancers (Fig. [85]3b). Moreover, the samples with the highest
ratio (top 5%) of apoptosis to repair set mutations completely lack
mutations in the repair set genes (whereas the samples with the lowest
ratio carry mutations in both sets, Fig. [86]3c). Indeed, TP53 shows
the strongest positive association with aneuploidy as the only gene
that is positively and significantly associated with aneuploidy in
gastrointestinal and endometrial tumors (Supplementary Fig. [87]9).
Nevertheless, excluding TP53 (as well as BRCA2) from the apoptosis
mutated gene set does not eliminate the association of the ratio
between the repair and apoptosis sets with aneuploidy (Supplementary
Fig. [88]10)
Fig. 3. DNA repair and apoptosis mutational sets correlate with aneuploidy.
[89]Fig. 3
[90]Open in a new tab
a Spearman correlation coefficients between aneuploidy and the
apoptosis and repair set load, and their ratio. Statistical
significance (Spearman rank-correlation P-value < 0.05) is indicated
with asterisk. b Boxplot comparing the aneuploidy distribution between
low (<1) and high (>1) apoptosis to repair set load ratio. Statistical
significance (Rank-sum P-value < 0.05) is indicated with asterisk.
Center lines indicate medians, box edges represent the interquartile
range, whiskers extend to the most extreme data points not considered
outliers, and the outliers are plotted individually. c Map of the
repair and apoptosis set mutations and levels of aneuploidy, for
samples with highest and lowest apoptosis to repair set load ratios
(top and bottom 0.05 quartiles).
Associations between MSI, aneuploidy, and clinical outcome
Our analyses show that gastrointestinal and endometrial cancers form a
separate class of tumors in which aneuploidy is anticorrelated with the
load of driver mutations. Furthermore, these tumors are characterized
by predominant mutations in DNA repair genes and a paradoxical, inverse
dependency between driver mutations and survival. Additionally, a
subset of tumors in this class shows high MSI. Similarly to the
previous findings for colorectal tumors^[91]23,[92]24, we demonstrate
an inverse association between MSI and aneuploidy across all
gastrointestinal and endometrial TCGA tumors, and a positive
association between MSI and driver mutations except for those in TP53
and APC genes (Supplementary Fig. [93]9). Accordingly, we derived a
third mutated gene set to represent the apparent mutual exclusion
between aneuploidy and MSI in these tumors, so that to simultaneously
maximize the positive association with MSI and the negative association
with aneuploidy, focusing on DDR and cancer driver genes (using a
feature selection process similar to that employed for the other sets;
see Methods for details). The 17 genes in the selected optimal set
reflect the tradeoff between aneuploidy and MSI in gastrointestinal and
endometrial cancers (Fig. [94]4a, b), and are strongly enriched in
mismatch repair and double strand break repair genes (MLH1, MSH2, PMS2,
DNA2, FBXO18, RAD21, and RPA1). The MSI-aneuploidy set was highly
predictive of MSI not only in the TCGA data on which it was trained,
but also in two independent test data sets for colorectal
adenocarcinoma (COADREAD, Receiver operating characteristic Area Under
the Curve (AUC) = 0.85 and 0.95), one test data set for stomach
adenocarcinoma (STAD, AUC = 0.85), and one for uterine corpus
endometrial carcinoma (AUC = 0.92, Fig. [95]4c). The high mutation load
of this set is associated with better survival across the integrated
patient cohort of all gastrointestinal and endometrial tumors
(Fig. [96]4d), and individually in each tumor type excluding rectal
adenocarcinoma (READ), where the sample size is likely to be a
confining factor (Fig. [97]4e).
Fig. 4. Mutated gene set reflecting the tradeoff between MSI and aneuploidy.
[98]Fig. 4
[99]Open in a new tab
a The mutations in the selected set (top panel) positively correlate
with MSI (middle panel) and negatively correlate with aneuploidy
(bottom panel) across gastrointestinal and endometrial tumor samples
(b) correlation between aneuploidy (x-axis) and the aneuploidy-MSI set
mutation load (y-axis). c ROC classification curves predicting MSI
using the aneuploidy-MSI set mutation load in the training data and
four test datasets. d Kaplan–Meier curves predicting overall survival
across gastrointestinal and endometrial tumor samples with high vs. low
number of aneuploidy-MSI set mutations, separated by the median. e
Kaplan-Meier curves predicting overall survival in each colon (COAD),
rectal (READ), stomach (STAD), uterine corpus (UCEC), and uterine (UCS)
carcinomas, for samples with high vs. low number of aneuploidy-MSI set
mutations separated by the median. The log-rank P-value are provided.
MSI has been associated with improved survival in gastrointestinal and
endometrial tumors^[100]25–[101]27, whereas chromosomal instability has
been linked to poor survival^[102]28,[103]29. Because of the strong
inverse associations observed between aneuploidy and MSI, we next
explored the individual contributions of MSI and aneuploidy to the
overall survival, compared with microsatellite-stable (MSS) diploid
tumors. We found that aneuploid tumors are associated with the worst
outcome, whereas no significant differences were observed between MSS
and MSI diploid tumors (Fig. [104]5a). These findings imply that the
favorable outcome associated with MSI could be only due to the diploid
karyotype nature of the MSI tumors, as opposed to being caused by MSI
as such. Supporting this notion, we found that MSI mutational
signatures, which expectedly exert negative associations with
aneuploidy, are associated with poor survival in diploid tumors (COSMIC
signatures 6, 14, 15, 20, 21, and 26^[105]30,[106]31, that have been
associated with MSI^[107]31,[108]32; see Supplementary Fig. [109]11).
The MSI-aneuploidy set mutation load was associated with improved
survival independently of MSI and aneuploidy, supporting the
considerable prognostic power of the mutations in this set
(Fig. [110]5b, c). To find out whether these survival correlates of MSI
and aneuploidy reflect differences in therapeutic vulnerabilities, we
obtained chemotherapy response data for this cluster of TCGA tumor
types. MSI has been previously linked to improved prognosis, but also
has been proposed as a marker of non-response to
chemotherapy^[111]26,[112]33–[113]35. Indeed, we found the rate of
complete or partial response to chemotherapy to be considerably higher
among MSS compared to MSI tumors, and for diploid vs. aneuploid tumors
(Fig. [114]5d). In accord with these observations, mutation load of the
MSI-aneuploidy set was higher in responders for some chemotherapeutic
agents (Fig. [115]5e). Single mutations in the MSI-aneuploidy set were
not highly predictive of the response to multiple chemotherapeutic
agents, so that the set load performed better than individual mutations
(Fig. [116]5f).
Fig. 5. Aneuploidy and MSI associations with overall survival and
chemotherapy response.
[117]Fig. 5
[118]Open in a new tab
a Kaplan–Meier overall survival curves for MSS tumors with high
aneuploidy (red curve, higher than median), MSI tumors (yellow curve),
and MSS tumors with low aneuploidy (blue curve, lower than median). b
Kaplan–Meier overall survival curves for MSS tumors with MSI-aneuploidy
set mutations (blue curve), MSI tumors with MSI-aneuploidy set
mutations (yellow curve) and MSS tumors with no MSI-aneuploidy set
mutations (red curve). c Kaplan–Meier overall survival curves for
diploid tumors with MSI-aneuploidy set mutations (blue curve), diploid
tumors with no MSI-aneuploidy set mutations (yellow curve) and
aneuploid tumors with no MSI-aneuploidy set mutations (red curve). The
log-rank P-value are provided. d Heatmap displaying the percent of
responders to each chemotherapeutic agent (rows) for tumors with
specific alterations (columns). e Boxplots presenting the
MSI-aneuploidy set mutation load for responders (blue, CR/PR) vs.
non-responders (red, SD/PD), for each chemotherapeutic agent. Center
lines indicate medians, box edges represent the interquartile range,
whiskers extend to the most extreme data points not considered
outliers, and the outliers are plotted individually. f AUC map (of ROC
classification curves) of individual MSI-aneuploidy set mutations
(columns) for predicting response to each chemotherapeutic agent
(rows). Source data are provided as a Source Data file.
Discussion
Although aneuploidy is a pervasive characteristic of cancer cells, the
molecular basis of aneuploidy and implications for patient prognosis
are not well understood for most cancers^[119]36,[120]37. Here, we
partition tumor types into two classes showing opposite associations of
driver mutations with aneuploidy and patients survival. These
association patterns reflect distinct sets of mutations in different
DDR pathways (Fig. [121]6). Specifically, the mutated gene set for the
gastrointestinal and endometrial tumors that are characterized by a
negative association between the driver mutation load and aneuploidy
consists primarily of various DNA repair genes (this association is not
limited to MSI tumors; Supplementary Fig. [122]12). In this class of
tumors, we also observed a paradoxical, negative association between
the driver mutation load and patient survival. Conceivably, this could
be caused by the multiple mutations in repair genes that introduce a
vulnerability to DNA damage. In contrast, for the rest of the analyzed
tumor types, where the association between driver mutations and
aneuploidy is positive, the mutational set is dominated by genes
encoding components of the apoptosis machinery and DNA damage-related
cell cycle checkpoints. An additional facet of cancer genome
instability is the inverse relationship between aneuploidy and MSI in
the gastrointestinal and endometrial tumors (Fig. [123]6), for which we
derived a third mutational set that was highly enriched in a distinct
set of repair genes. Crucially, the mutational sets derived here
strongly correlate with patient survival. In particular, the ratio of
the mutation loads for the apoptotic set to that of the repair set was
found to be a universal correlate of survival: a high ratio corresponds
to poor survival, suggesting that this ratio could have a prognostic
value.
Fig. 6. A schematic, conceptual depiction of the emerging links between
patient survival, driver mutation load and aneuploidy in the two tumor
classes.
[124]Fig. 6
[125]Open in a new tab
a Driver mutations, aneuploidy, patient survival, and mutation sets in
the two tumor classes. The mutation sets derived to predict high driver
mutations load are shown by circles. The inverse relationship between
aneuploidy and MSI is demonstrated for the gastrointestinal and
endometrial scheme, with the derived MSI-aneuploidy set. b The ratio of
the mutation loads in the apoptosis vs repair sets as universal
predictor of patient survival.
Our pan-cancer analysis connects different genomic aberrations with
distinct DDR pathways and segregates gastrointestinal and endometrial
tumors into a separate class, where tumorigenesis might be
predominantly driven by defects in specific DNA repair pathways. It has
been established previously that mutated apoptosis and DNA damage
checkpoint signaling pathways sustain growth with genomic
abnormalities^[126]38–[127]40. The results described here suggest that
major deficiencies in DNA repair permit accumulation of oncogenic
mutations but not aneuploidy. This finding is in agreement with the
inverse relation between MSI and aneuploidy in colorectal
tumors^[128]41, and with the diploid karyotype of NER-deficient skin
cancer^[129]42. The inverse relationship between repair and aneuploidy
might reflect a direct functional link, whereby intact DNA repair
pathways promote the emergence of aneuploidy, or conversely, survival
and reproduction of aneuploid cells requires active repair. One
possible mechanism underlying such a functional link could be chromatin
remodeling. While chromatin relaxation is crucial for DNA
repair^[130]43,[131]44, condensed chromatin structure in necessary for
chromosomal segregation^[132]45. Tumor cells that are actively engaged
in DNA repair might maintain relaxed chromatin structures that hinder
chromosomal segregation and increase aneuploidy, whereas tumor cells
with dysfunctional repair systems would preserve condensed chromatin
that sustain proper chromosomal segregation and preclude aneuploidy.
Indeed, several of the genes in the repair set are involved in
chromatin remodeling, such as ALC1 (CHD1L), PARP1, and DDB2. In
particular, ALC1 is a chromatin remodeling enzyme that relaxes
chromatin at early stages of DNA repair^[133]46 through PARP1 and DDB2
recruitment^[134]47. Together with the inclusion of several other
chromatin remodeling-associated genes in the repair set (POLE3, EPC2,
BRCC3, HMGA1, and HMGB1) and the MSI-aneuploidy set (EP300, CUL4B,
RPA1, ARID1A, and NFATC4), this could suggest that inhibition of
chromatin relaxation resulting from impairing mutations in DNA repair
genes prevents the emergence of aneuploidy in tumor cells.
There is, obviously, a complex relationship between aneuploidy and
patient prognosis. Although aneuploidy has been associated with poor
patient survival and linked with intrinsic drug
resistance^[135]28,[136]48, evidence is accumulating that extreme
aneuploidy might also be associated with improved patient
outcome^[137]29,[138]49,[139]50. We demonstrate that aneuploidy is
compatible with impairment of apoptotic and DNA damage checkpoint
signaling pathways^[140]51–[141]53, but is suppressed by inactivation
of DNA repair pathways. Furthermore, although MSI has been previously
associated with favorable prognosis, the present findings indicate that
this connection could result from the lack of aneuploidy in the MSI
tumors rather than from any effect of MSI as such. These findings are
in agreement with the observed association of aneuploidy with
multi-drug resistance^[142]54,[143]55. Indeed, the loss of DNA repair
sensitizes cells to various drugs that induce DNA damage, whereas cells
deficient in checkpoint and apoptotic signaling in response to damage
lack this type of vulnerability. In addition, MSI tumors show enhanced
immune infiltration levels and improved response to
immunotherapy^[144]56,[145]57, whereas the reduced mutational load in
gastrointestinal and endometrial aneuploid tumors is likely to restrict
the benefits of immunotherapy and several targeted therapies. However,
the mutual exclusivity between the loss of DNA repair and aneuploidy
raises another possibility, namely, that co-occurrence of aneuploidy
with defective DNA repair is lethal, suggesting a therapeutic potential
for targeting repair processes in aneuploid cells.
In summary, we reveal here alternative regimes of tumorigenesis that
involve different, either synergistic or mutually exclusive
relationships between driver mutations and chromosomal aberrations in
different cancer types (Fig. [146]6). By using large human cancer
cohorts, we show that these distinct tumorigenic regimes are
underpinned by unique DDR mutational set that appear to govern
accumulation of driver mutations and aneuploidy. The derived mutational
sets are predictive of patient survival and suggest specific
vulnerabilities of different cancer types that might have therapeutic
potential.
Methods
Data
TCGA samples of primary and metastatic solid tumors were selected for
analysis. The complete mutational data for 32 tumor types in each TCGA
study was obtained from the UCEC Xena browser^[147]58, considering all
non-silent mutations. Arm-level gain or loss values were obtained for
each TCGA sample^[148]11, where the ploidy was determined using the
ABSOLUTE algorithm^[149]59. Each segment was designated as amplified,
deleted, or neutral compared with the ploidy of the corresponding
sample. The scores assigned to each arm were −1 if lost, +1 if gained,
and 0 otherwise. The aneuploidy score for each tumor is calculated as
the sum of altered arms, within a range of 0 to 39 (long and short arms
for each non-acrocentric chromosome, and only long arms for chromosomes
13, 14, 15, 21, and 22). Sample-wise clinical data was obtained from
the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR^[150]14).
Altogether, 8686 TCGA samples containing all data types including
somatic point mutations, aneuploidy scores and clinical data were
analyzed (Table [151]1).
Cancer driver genes were obtained^[152]15, for the drivers analysis a
list of pan-cancer driver mutations were used, encompassing the 200
driver mutations^[153]15 that are categorized pan-cancer drivers (i.e.
general drivers and not associated with a subgroup of the tumor types,
Supplementary Data [154]3).
Microsatellite Instability (MSI) classification was obtained^[155]60
for uterine corpus endometrial, stomach, colon and rectal carcinomas
(UCEC, STAD, COAD, and READ, respectively), for a total of 718 samples
of the 8686 with available molecular and clinical data. Drug response
data for all drug-patient pairs was obtained^[156]61,[157]62 for these
tumor types (considering drugs with sufficient number of samples,
n > 15), and categorized into responders (complete or partial response,
CR/PR) and non-responders (progressive or stable disease, PD/SD).
To test the MSI-aneuploidy set for MSI prediction in independent test
sets, four additional tumor mutational datasets were obtained with MSI
classification including two datasets of colorectal adenocarcinoma
(n = 619^[158]62 and 72^[159]63 with 91 and 15 MSI samples,
respectively), one for stomach adenocarcinoma (n = 100^[160]64 with 10
MSI samples) and one of uterine corpus endometrial carcinoma
(n = 195^[161]65 with 28 MSI samples).
Tumor clustering
Hierarchical clustering of driver-aneuploidy associations in each tumor
type, with the average linkage function and Euclidian distance metric,
was performed to classify tumors based on these associations. The
clustering was applied to a matrix of correlation coefficients between
each driver mutation and aneuploidy (rows) in each tumor type
(columns), where missing values (corresponding to missing mutations in
the datasets) were assigned the mean correlation value for each tumor
type.
DDR mutational sets predicting driver mutations load
DDR mutational sets predictive of the driver mutational load were
derived for each tumor cluster individually using a Genetic Algorithm
(GA) search to produce sets of DDR mutations predictive of drivers load
in individual tumor types. For each tumor type, 100 repetitions of the
genetic algorithm were run, where the initial population of size of
[MATH: n4
:MATH]
(n is the sample size of the tumor type), was (a) initialized randomly
with the p = 0.05 probability of each mutation in the population set.
The objective set was the Spearman correlation coefficient ρ between
the population DDR set load and the driver mutation load, which was (b)
evaluated for each item in the population (for its unique set of DDR
mutations) on the true population of tumor samples. Then, the top half
of the population with the highest ρ with the driver mutation load in
the test set was (c) selected for reproduction, where randomly selected
pairs from this selected half of the population were chosen for (d)
crossover, with p = 0.05 × M[i] probability of mutations in the
crossover process (M[i] is the number of mutations of item in the
population), until a population size of
[MATH: n4
:MATH]
was reached. Twenty iterations of the steps (b–d) were performed, and
the best solution (set of mutations with the highest correlation with
the load of drivers), was retained. When 100 iterations were completed,
the solutions obtained were evaluated to generate a selection score for
each DDR mutation m[i].
[MATH:
Selecti<
mi>onscoremi=
∑iterationjIij∑mutationkI<
/mrow>kj :MATH]
where I[ij] is the selection of g[i] in iteration j, thus giving higher
weight to m[i] that is selected in iterations with fewer selected
mutations.
A binomial P-value p[t] was assigned to each DDR mutation via the
resulting scores distribution for each tumor type t. For each DDR gene,
the P-values assigned to tumors in each cluster were combined into test
statistic X^2 using the Fisher method
[MATH: X2CT2~−2∑t=1CTlnpt :MATH]
where pt is the P-value assigned to a mutation in tumor type t, and CT
is the number of tumor types in a cluster.
The X^2 P-values were then derived for each mutation in the two tumor
classes individually. The final set derived for each cluster consisted
of mutations with significant X^2 P-values (with α < 0.1 cutoff) only
in the corresponding tumor class (i.e. not significantly associated
with the other class; see Supplementary Data [162]4 and Fig. 1[163]3).
Using a stricter cutoff did not change the selected genes in the
apoptosis set, and yielded 45 of the 53 genes in the repair set, with
performance similar to the original one (Supplementary Fig. [164]14).
Repeating this analysis without limiting the search for enriched
mutated gene sets to DDR genes (i.e. starting from all genes) did not
yield a DNA-repair enriched set of genes for the gastrointestinal and
endometrial tumors. This is likely to be the case because tumors with
impaired mismatch repair contain mutations in many different genes,
thus making in difficult to identify the initial set of mutated DNA
repair genes. By contrast, the set selected for other tumor types was
still enriched with genes involved in apoptotic pathways. Crucially,
the ratio between these sets showed similar associations with
aneuploidy and overall survival rates as the DDR-limited search
(Supplementary Fig. [165]15).
MSI-aneuploidy set
To derive a mutational set that would be simultaneously predictive of
MSI and low aneuploidy, different sets predicting MSI and low
aneuploidy were obtained independently. The genetic algorithm described
above was applied to the cluster of gastrointestinal and endometrial
tumor samples using (a) 100 repetitions aiming to maximize the Spearman
correlation coefficient ρ between each set and the aneuploidy level and
(b) 100 repetitions aiming to maximize the performance (AUC of ROC
curve) of each set in predicting the MSI status. Mutations
significantly selected for both tasks (with combined X^2 P-value < 0.1
for the selection scores over 100 repetitions for both (a) and (b))
were chosen to compose the final MSI-aneuploidy set.
Statistical analysis
Boxplots and comparisons: for all boxplots, center lines indicate
medians, box edges represent the interquartile range, whiskers extend
to the most extreme data points not considered outliers, and the
outliers are plotted individually. Points are defined as outliers if
they are greater than q[3] + w × (q[3]–q[1]) or