Abstract
BRCAness has important implications in the management and treatment of
patients with breast and ovarian cancer. In this study, we propose a
computational framework to measure the BRCAness of breast and ovarian
tumor samples based on their gene expression profiles. We define a
characteristic profile for BRCAness by comparing gene expression
differences between BRCA1/2 mutant familial tumors and sporadic breast
cancer tumors while adjusting for relevant clinical factors. With this
BRCAness profile, our framework calculates sample-specific BRCA scores,
which indicates homologous recombination (HR)-mediated DNA repair
pathway activity of samples. We found that in sporadic breast cancer
high BRCAness score is associated with aberrant copy number of HR genes
rather than somatic mutation and other genomic features. Moreover, we
observed significant correlations of BRCA score with genome instability
and neoadjuvant chemotherapy. More importantly, BRCA score provides
significant prognostic value in both breast and ovarian cancers after
considering established clinical variables. In summary, the inferred
BRCAness from our framework can be used as a robust biomarker for the
prediction of prognosis and treatment response in breast and ovarian
cancers.
Introduction
Breast cancer is the most common type of cancer in female patients,
with one out of eight women developing breast cancer in their
lifetime^[30]1. Many factors have been found to be associated with the
increased risk of this disease including family history^[31]2. Familial
breast cancer represents a minor percentage of all breast cancer cases
and can occur in patients with one or more closely related family
members diagnosed with breast, ovarian, or related cancer^[32]2,[33]3.
Approximately 25% of familial breast cancer cases may be attributed to
germline mutations in two major breast cancer susceptibility genes,
BRCA1 ^[34]4 and BRCA2^[35]5. Germline mutations in BRCA1 and BRCA2
genes exhibit high penetrance and confer a 60–80% and 40–85% lifetime
risk of developing breast cancer, respectively^[36]6. In ovarian
cancer, BRCA1 and BRCA2 germline mutations will confer a risk of 40–60%
and 30%, respectively^[37]7. Other than germline mutations in these two
genes, many other genetic mutations or variants can contribute to
familial breast cancer including germline mutations with high (e.g.,
TP53 and PTEN) and low penetrance (e.g., ATM and BRIP1), as well as
low-penetrance genetic variants (e.g., single-nucleotide polymorphisms
(SNPs))^[38]8.
BRCA1 and BRCA2 are essential regulators of the homologous
recombination (HR) pathway that are involved in the repair of
double-stranded DNA breaks^[39]9,[40]10. HR-dependent DNA repair
restores damaged DNA sequences to its original state without
introducing DNA mutations. When this pathway is inactivated, e.g., as a
result of BRCA1/2 mutations, alternative DNA repair pathways such as
non-homologous end joining (NHEJ) become the major pathways utilized
for repairing double-strand DNA breaks. These alternative repair
mechanisms are error-prone and lead to rapid accumulation of somatic
mutations which increase the risk of carcinogenesis^[41]11,[42]12.
Interestingly, somatic mutations in BRCA1/2 are rarely observed in
sporadic breast cancers^[43]13,[44]14. However, defects in HR-dependent
DNA repair can arise through other mechanisms, resulting in a
“BRCA-like” phenotype. For this reason, the concept of “BRCAness” has
been introduced to describe this shared phenotype between sporadic
cancers and familial cancers with BRCA1/2 mutations^[45]15,[46]16.
BRCAness has important implications in the management and treatment of
patients with sporadic breast and ovarian cancer^[47]15–[48]18. It has
been shown that tumors with defects in the HR-dependent DNA repair
pathway are hypersensitive to alkylating, platinum-based chemotherapies
that generate DNA interstrand crosslinks and induce double-stranded DNA
breaks during crosslink removal^[49]19,[50]20. However, tumors with
deficient BRCA1 activity are not sensitive to mitotic spindle poisons
such as the taxanes and vincristine^[51]21. This is because that
spindle disruption caused by these agents can induce apoptotic cell
death in BRCA-proficient but not in BRCA-deficient tumors. It has been
shown that downregulation of BRCA1 gene expression in ovarian cancer
cell lines increases their sensitivity to platinum treatment but leads
to resistance to antimicrotubule agents^[52]22,[53]23. In addition,
tumors with deficient BRCA1/2 are also sensitive to poly (ADP-ribose)
polymerase (PARP) inhibitors as a result of synthetic
lethality^[54]24,[55]25. In particular, PARP is involved in another DNA
repair pathway called base excision repair (BER) that repair
single-strand DNA breaks^[56]26. Inhibition of PARP results in
accumulation of DNA single-strand breaks, replication fork collapse and
double-strand DNA repair that are lethal in tumors with deficient HR
pathway.
The hallmarks of BRCAness are elevated genomic instability and
deficient HR pathway activity. Accordingly, there are two strategies to
determine the BRCAness of sporadic breast tumor samples. The first
strategy is to classify BRCA-like and non-BRCA-like samples based on
copy number variation (CNV) data. Classification models have been
constructed by selecting genomic regions with differential CNV between
familial (with BRCA1/2 germline mutations) and sporadic breast cancer
samples, and then applying these models to assess BRCAness in sporadic
samples^[57]27. These methods assume that there exist ‘hot’ genomic
regions with recurrent CNV shared by the majority of BRCA-deficient
familial and BRCA-like sporadic samples. However, this assumption may
not hold when considering the high heterogeneity of sporadic tumors.
The second strategy is to define gene signatures that characterize HR
deficiency and apply them to identify HR-deficient sporadic tumor
samples. Konstantinopoulos et al.^[58]28 defined a BRCAness gene
signature by comparing transcriptomic profiles between BRCA1/2 mutant
and sporadic ovarian tumor samples, and applied it to classify sporadic
ovarian tumors. Their approach demonstrated that samples with high
levels of BRCAness were associated with improved survival. A related
approach involves generating BRCAness gene expression profiles from
breast cancer cell line with RNA-mediated inactivation of HR pathway
genes (e.g. BRCA1, RAD51 and BRIT1)^[59]29. However, our previous study
using this approach showed that the resulting gene expression profiles
from these knockdown cell line were more likely to reflect the reduced
proliferation of cells rather than HR deficiency^[60]30.
In this study, we propose a robust statistical framework to
characterize a BRCAness gene expression profile to interrogate BRCA
activity in sporadic breast cancer samples. The BRCAness profile is
generated by comparing gene expression between BRCA1/2 mutant familial
tumors with sporadic breast tumors. In addition, this statistical model
used to define the profile takes into account clinical factors that
could explain differences in gene expression, thus effectively
isolating the expression changes most likely to be induced by varying
levels of BRCAness. This characteristic BRCAness profile utilizes all
genes rather than a gene signature composed of a selected group of
genes to achieve robust statistical performance. Application of this
framework to The Cancer Genome Atlas (TCGA) breast cancer omics data
revealed high heterogeneity of BRCAness mechanisms in sporadic breast
cancer. Our results indicate that in sporadic breast cancer, copy
number variation (CNV), especially deletions, contributes more to the
inactivation of HR pathway compared to somatic mutations, DNA
methylation, and expression changes in BRCA-like samples. Other than
BRCA1/2, CNV in other HR genes play a more important role to reduce HR
pathway activity in sporadic breast tumors. We also identified several
other HR pathway genes with CNV associated with BRCAness, suggesting
that other members of the axis may contribute more to HR pathway
activity. Moreover, the inferred BRCA score is predictive of clinical
outcomes of patients with breast cancer, providing a potential
prognostic marker. In addition, this BRCAness profile, although being
defined for breast cancer, is also applicable to ovarian cancer.
Results
Schematic overview of our study
Our computational framework starts by comparing familial breast tumors
carrying either BRCA1 or BRCA2 mutations with sporadic cancer samples
to generate a BRCAness characteristic profile (Fig. [61]1). This
comparison adjusts for several clinical factors including age, grade,
tumor size, estrogen receptor (ER) status, and Human Epidermal Growth
Factor Receptor 2 (HER2) status to ensure that the BRCAness
characteristic profile reflects BRCA1/2 mutation status rather than
differences in the distribution of these clinical variables across
patients. Given a tumor gene expression dataset, this BRCAness profile
calculates a sample-specific BRCA score for each individual patient in
the dataset by using the BASE algorithm, which measures the similarity
between a patient’s tumor expression profile and the BRCAness
profile^[62]31. Patients with higher BRCA scores are more similar to
have BRCA1/2 germline mutations in terms of their expression profiles,
and thereby more likely to have defective HR pathway activity.
Figure 1.
[63]Figure 1
[64]Open in a new tab
Overview of our computational framework. In short, we first compared
the gene expression profiles between familial and sporadic breast
tumors considering related clinical factors (e.g. age, grade, tumor
size, ER status and Her2 status) to generate a BRCAness profile.
Second, by integrating gene expressions of given breast cancer samples,
we calculated sample-specific BRCA scores. The scores inferred the
BRCAness of patients with the higher score the higher likelihood to be
BRCAness. Then, we showed that the BRCA score classifies familial from
sporadic breast tumors, correlates with genomic instability, predicts
patients’ survival and predicts chemotherapy response. Lastly, we
further applied the BRCAness profile defined with breast cancer
profiles to ovarian cancer. The corresponding BRCA scores also can
classify familial from sporadic samples and predict prognosis.
Defining characteristic weight profiles that encode BRCAness
We utilized the data generated by Larsen et al.^[65]32 to define three
BRCAness characteristic profiles (denoted as BRCA1-, BRCA2- and
BRCA1/2-based profiles) by respectively comparing BRCA1-, BRCA2- or
BRCA1/2-mutant (both BRCA1 and BRCA2) familial samples with sporadic
samples. In a BRCAness profile, each gene was assigned a weight based
on the difference in expression it exhibits between BRCA1/2-mutated
familial from sporadic breast cancer samples. Namely, genes with high
weights were significantly up- or down-regulated in BRCA1/2-mutated
samples after adjusting for several clinical variables that may be
potential confounders. We selected 300 genes with the highest positive
(up-regulated in BRCA1/2-mutant) and 300 genes with the highest
negative (down-regulated in BRCA1/2-mutant) weights in the BRCAness
profile and identified the enriched biological pathways. Our results
indicate that cell cycle associated and DNA replication pathways are
significantly enriched in the up-regulated genes (Supplementary
Table [66]S1). In addition, the same analyses were performed using top
weighted genes in BRCAness profiles defined based on BRCA1 versus
sporadic and BRCA2 versus sporadic profiles and obtained similar
results. These results are consistent with the known functions of BRCA1
and BRCA2 (Supplementary Table [67]S1).
Calculated BRCA score discriminates familial from sporadic breast tumors
We then examined whether BRCA score could distinguish familial breast
cancer patients from sporadic patients. Because the Larsen et al.
dataset^[68]32 was utilized to define three BRCAness characteristic
profiles, we first compared their performance using this dataset. We
calculated sample-specific BRCA scores for the 33 BRCA1-mutant familial
samples, the 22 BRCA2-mutant familial samples, and the 128 sporadic
samples using the BRCA1, BRCA2 and the BRCA1/2- profiles, respectively
(Fig. [69]2a). We found that BRCA1-mutant (Mann-Whitney Wilcoxon test
P = 6e-13) and BRCA2-mutant (Mann-Whitney Wilcoxon test P = 6e-4)
familial samples have significantly higher BRCA scores than sporadic
samples when using all three BRCAness profiles (Fig. [70]2a). To
further evaluate the predictive accuracy of the BRCA score, we trained
a binary classification model to classify familial breast tumors from
sporadic tumors (Fig. [71]2b). When using the BRCA1-based profile, the
calculated BRCA scores could clearly discriminate familial BRCA1-mutant
tumors from sporadic ones with an area under the curve (AUC) 0.901
(Fig. [72]2b, left). Similarly, the BRCA scores were able to
distinguish familial BRCA2-mutant tumors from sporadic ones
(AUC = 0.718), and all familial BRCA1/2-mutant tumors from sporadic
ones (AUC = 0.828). Consistent observations were showed in the
classifications utilizing the BRCA2-based (Fig. [73]2b, middle) and the
BRCA1/2-based (Fig. [74]2b, right) BRCAness profiles. These results
indicate that the calculated BRCA score can distinguish familial breast
cancer patients from sporadic ones with high accuracy and can capture
differences in the BRCAness phenotype. A high BRCA score implies high
likelihood of carrying a BRCA1/2 germline mutation and thus a more
deficient HR pathway. Notably, the calculated BRCA scores using the
BRCA1/2-based profile achieved the best classification accuracy when
comparing familial breast tumors from sporadic ones (Fig. [75]2b,
right, AUC = 0.861 for BRCA1 vs. sporadic, ACU = 0.809 for BRCA2 vs.
sporadic and AUC = 0.84 for BRCA1/2 vs. sporadic). Based on these
results, we applied the BRCA scores calculated using the BRCA1/2-based
profile for subsequent analyses.
Figure 2.
[76]Figure 2
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BRCA score classifies familial from sporadic breast cancer patients.
(a) Boxplot for comparisons of BRCA scores in germline mutate-BRCA1
(green box), germline mutate-BRCA2 (tawny box) and sporadic (blue box)
breast cancer samples using gene expression of germline mutate-BRCA1
(left), germline mutate-BRCA2 (middle), germline mutate-BRCA1/2 (both
BRCA1 and BRCA2, right) as reference to compare with those of sporadic
ones, respectively. Mann-Whitney Wilcoxon test P-values were calculated
to show the differences of BRCA scores between germline mutate-BRCA1
and sporadic breast cancer samples, germline mutate-BRCA2 and sporadic
breast cancer samples. (b) ROC curves for the accuracy of classifying
familial from sporadic breast cancer patients using BRCA scores
calculated by comparing gene expression of germline mutate-BRCA1
(left), germline mutate-BRCA2 (middle), germline mutate-BRCA1/2 (right)
as reference to sporadic ones, respectively. Black curve: comparison of
patients with germline BRCA1 mutations to sporadic ones. Magenta curve:
comparison of patients with germline BRCA2 mutations to sporadic ones.
Cyan curve: comparison of patients with germline BRCA1 and BRCA2
mutations to sporadic ones. AUC scores were shown. The BRCA scores
calculated based on germline BRCA1/2 mutations achieved the best AUCs.
(c) Boxplot for BRCA scores calculated by integrating BRCAness profile
and gene expression profile offered by [78]GSE50567 which contains
profiles for familial BRCA1 and BRCA2 (BRCA), other familial with
non-BRCA mutations, sporadic and normal breast cancer samples. Whitney
Wilcoxon test P-values were listed. (d) Same with (c) but for
[79]GSE27830 which contains profiles for four familial breast cancer
samples including BRCA1, BRCA2, CHEK2 and other mutations
(non-mutations of aforementioned genes). (e) Same with (c) but for
[80]GSE19177 which contains profiles for three groups of familial
breast cancer samples including BRCA1, BRCA2 and non-BRCA1/2 mutations.
Moreover, we utilized the [81]GSE50567^[82]33 dataset to further test
the ability of the BRCA score to classify breast cancer patients. This
dataset contained 12 BRCA1- and 1 BRCA2-mutated hereditary breast
tumors, 8 BRCAx (non-BRCA1/2 mutations, donated as non-BRCA) hereditary
breast tumors, 14 sporadic breast cancer samples and 6 normal samples.
However, we found that 5 sporadic and 1 BRCAx samples had methylated
BRCA1 promoters which could result in transcriptional silencing of
BRCA1 ^[83]34 and a reduction in HR pathway activity. Therefore, we
combined these 6 patients with the 12 BRCA1- and 1 BRCA2-mutated
hereditary breast cancer patients into one group (donated as “BRCA”).
The comparison of BRCA scores showed that patients in the BRCA group
have higher BRCA scores than normal breast samples (Fig. [84]2c,
Mann-Whitney Wilcoxon test P = 6e-06) and sporadic tumors (Mann-Whitney
Wilcoxon test P = 0.04). Moreover, patients carrying BRCA mutations had
higher BRCA scores than the other familial tumors with non-BRCA
mutations (Mann-Whitney Wilcoxon test P = 7e-04). Furthermore, we
observed similar results in two additional familial breast cancer
datasets. The [85]GSE27830^[86]35–[87]37 dataset provided gene
expression profiles for 155 familial primary breast cancer samples
including 47 BRCA1-, 6 BRCA2-, 26 CHEK2- mutant samples and 76 samples
without mutations in these three genes. Due to the limited number of
BRCA2-mutant patients, we merged BRCA1- and BRCA2-mutant patients into
one group. Based on the BRCA1/2-based profile, we calculated BRCA
scores to patients in this dataset and found that patients with BRCA1/2
mutations have significantly higher BRCA scores than those with CHEK2
(Mann-Whitney Wilcoxon test P = 2e-05) and other gene (Mann-Whitney
Wilcoxon test P = 6e-05) mutations (Fig. [88]2d). We found similar
results in the [89]GSE19177 dataset^[90]38,[91]39 which contains
expression profiles for 19 BRCA1, 30 BRCA2 and 25 non-BRCA1/2 mutation
familial breast cancer samples (Fig. [92]2e). Again, patients with
BRCA1 mutations had higher BRCA scores than those with non-BRCA1/2
mutations. These observations suggest that the calculated BRCA score is
an effective classifier to distinguish familial breast tumors from
sporadic tumors and to identify BRCA1 or BRCA2 mutated patients within
familial breast cancer patients. The score is negatively correlated
with the deficient HR pathway activity caused by BRCA1 or BRCA2
mutation.
Association of BRCA score with genomic features
Because BRCAness significantly correlates with genomic
instability^[93]15, we extended our computational framework to TCGA
breast cancer datasets to further examine the correlation of BRCA score
with genomic features. First, we investigated the association between
BRCA score and somatic mutations. By comparing the difference in BRCA
score between mutated and non-mutated samples for each gene, we found
that somatic mutation status of three genes, TP53
(Mann-Whitney-Wilcoxon Test P = 2e-30), PIK3CA (Mann-Whitney-Wilcoxon
Test P = 1e-16) and CHD1 (Mann-Whitney-Wilcoxon Test P = 2e-17), were
significantly correlated with the BRCA score. Specifically, patients
with higher BRCA scores were more likely to carry TP53 mutations
(Supplementary Fig. [94]S1). In contrast, patients with lower BRCA
scores were more likely to harbor mutated PIK3CA and CHD1
(Supplementary Fig. [95]S1). Then, we examined the overall association
between BRCA score and CNV in breast cancer samples. According to TCGA
CNV dataset, we divided genes into deletion (CNV < 1.2), normal and
duplication (CNV > 2.8) groups. Ranking TCGA patients based on their
BRCA scores, we found that high BRCA scores correlate with CNV
(Supplementary Fig. [96]S1). Last, by calculating the z scores across
CpG sites for each patient, we found that BRCA scores are overall
associated with DNA methylation (Supplementary Fig. [97]S1).
Then, we compared the BRCA scores of TCGA breast cancer samples with
their CNV burden and mutation burden, respectively. We observed that
patients with a higher BRCA score had higher CNV burden (Fig. [98]3a,
r = 0.624) and higher mutation burden (Fig. [99]3b, r = 0.409). These
results imply that the BRCA scores are positively correlated with
genomic instability. This might be because a high BRCA score indicates
a defective HR DNA repair pathway which can result in a more abnormal
genome. Since BRCA scores infer HR pathway activity, we next focused on
27 HR genes defined by the KEGG database^[100]40 to reveal the
correlation between BRCA score and their genomic features. First, we
ranked TCGA breast cancer patients based on their BRCA scores in a
decreasing order. Second, we compared differences in CNV, DNA
methylation, and gene expression of the 27 HR genes between top ranked
patients (top 1% to 20%) and low ranked patients (remaining patients)
(See Methods). We observed that patients with higher BRCA scores
exhibited significant copy number deletion (CNV < 1.2) of the 27 HR
genes compared to those with lower scores (Fig. [101]3c). In contrast,
we observed no significant changes in DNA methylation or gene
expression of the 27 HR genes between top ranking and low ranking
patients. These results suggest that CNV is the primary driver of HR
pathway inactivation, while DNA methylation and gene expression play
minor roles in determining pathway activity. Moreover, we compared the
BRCA scores for patients with 27 HR genes copy number deletion
(CNV < 1.2) to the rest patients. Higher BRCA scores were observed in
patients with deletion compared to the others (Fig. [102]3d,
Mann-Whitney-Wilcoxon Test P = 9e-12). When only focusing on the two
key pathway genes, BRCA1 and BRCA2, we observed that copy number
deleted patients have higher BRCA scores (Fig. [103]3d,
Mann-Whitney-Wilcoxon Test P = 7e-04). However, the p-value for
comparison using BRCA1 and BRCA2 was lower than that using the 27 HR
genes which suggests that the deletion of other HR pathway genes might
contribute more to effect HR pathway activity (Fig. [104]3d). In
addition to CNV, we observed consistent results when comparing somatic
mutation status with BRCA score. Namely, patients with mutations in the
27 HR genes (n = 68) exhibited higher BRCA scores compared to patients
without a mutation in either of the 27 HR genes (Fig. [105]3e,
Mann-Whitney-Wilcoxon Test P = 0.02). Moreover, patients with mutated
BRCA1 or BRCA2 (n = 25) exhibited higher BRCA scores compared to
non-mutant samples (Fig. [106]3e, Mann-Whitney-Wilcoxon Test
P = 0.006).
Figure 3.
[107]Figure 3
[108]Open in a new tab
BRCA score correlates with breast cancer genomic features. (a)
Correlation between BRCA scores and CNV burden. Spearman correlation
coefficient and corresponding P-value were listed. (b) Correlation
between BRCA scores and log-10 transferred mutation burden. Spearman
correlation coefficient and corresponding P-value were listed. (c) We
ranked TCGA breast cancer patients based on their BRCA scores in a
decreasing order. We then compared the difference between top ranked
(from 1% to 20%) patients and the remaining according to CNV, DNA
methylation and gene expression of the 27 HR genes. The differences
were calculated through the Mann-Whitney-Wilcoxon Test. Negative log-10
transferred P-values were shown. (d) Boxplot for BRCA scores in
patients with 27 HR genes deletions and scores in the rest patients.
And Boxplot for BRCA scores in patients with only BRCA1 and BRCA2
deletions and scores in the rest patients. Mann-Whitney-Wilcoxon Test
P-values were listed. (e) Boxplot for BRCA scores in patients with 27
HR genes mutations and scores in the rest patients. And Boxplot for
BRCA scores in patients with only BRCA1 and BRCA2 mutations and scores
in the rest patients. Mann-Whitney-Wilcoxon Test P-values were listed.
BRCA score predicts breast cancer patient prognosis
We next sought to evaluate whether the BRCA score could predict
survival outcomes of breast cancer patients. First, we calculated the
BRCA scores using our method for patients from the METABRIC cohort,
which provides the most comprehensive breast cancer gene expression
dataset that is accompanied by exhaustive clinical records for 1,992
primary breast cancer patients^[109]41. We next compared the
association of BRCA scores with clinic pathological characteristics
including grade and stage which are well-established clinical
indicators of disease severity. Our results show that the calculated
BRCA score was positively correlated with tumor grade, with higher
grade tumors showing higher BRCA scores (Mann–Whitney U-test,
P = 3e-88; Fig. [110]4a). Likewise, we observed that patients with
stage 2–4 tumors exhibited significantly higher BRCA scores compared to
patients with stage 1 tumors (Mann–Whitney U-test, P = 2e-05;
Fig. [111]4b). These results indicate that BRCA score reflects the
aggressiveness of tumors and can serve as an indicator of disease
progression.
Figure 4.
[112]Figure 4
[113]Open in a new tab
BRCA score predicts prognosis for breast cancer patients. (a) Boxplot
for BRCA scores of patients in different grades. Mann–Whitney U-test
P-value was listed. (b) Same as (a) but for different cancer stages.
(c) Distribution of BRCA scores based on breast cancer subtypes. Then,
we ranked the BRCA scores and showed the correlations with TP53
mutation, ER status, PR status, Her2 status, triple negative breast
cancer (TNBC) and molecular breast cancer subtypes. We compared BRCA
scores in TP53 mutation vs. TP53 wild type, ER+ vs. ER−, PR+ vs. PR−,
Her2+ vs. Her2−, TNBC vs. the other breast cancer samples, Basal-like
vs. non-Basal-like, HER2-Enriched vs. non-HER2-Enriched, Luminal A vs.
non-Luminal A, Luminal B vs. non-Luminal B and Normal vs. non-Normal
patients. P-values were calculated using Mann–Whitney Wilcoxon test.
(d–g) Kaplan-Meier plots for BRCA scores comparison of the (d)
discovery, (e) validation datasets in the METABRIC dataset, (f)
Ur-Rehman dataset, (g) Vijver dataset. Patients with low BRCA scores
(green curve) had better survival than those with high BRCA scores (red
curve). Hazard ratio (HR) and log-rank test P-value were shown.
Moreover, we ranked the BRCA scores of patients from high to low and
analyzed the distribution of patients based on several variables
including TP53 mutation, ER status, progesterone receptor (PR) status,
HER2 status, triple negative status, and molecular subtype
(Fig. [114]4c and Supplementary Fig. [115]S2). Patients carrying TP53
mutations were tended to have higher BRCA scores compared to those with
wild-type TP53 (Mann–Whitney Wilcoxon test P = 2e-12) which is
consistent with our result in the TCGA dataset. Moreover, we found that
ER+ patients have a higher likelihood of lower BRCA scores compared to
those with ER− (Mann–Whitney Wilcoxon test P = 2e-34). Similar,
PR+ patients had lower calculated BRCA scores than those with PR−
(Mann–Whitney Wilcoxon test P = 9e-31). In contrast, patients with
HER2+ tended to have higher BRCA scores (Mann–Whitney Wilcoxon test
P = 5e-12). Moreover, triple negative breast cancer (TNBC) was more
likely to be present in patients with high BRCA scores (Mann–Whitney
Wilcoxon test P = 1e-28). Furthermore, we compared the BRCA scores in
patients with Basal, HER2-enriched, Luminal A, Luminal B, or
Normal-like tumors. Patients with higher BRCA scores had higher risk to
be predicted as Basal-like (Mann–Whitney Wilcoxon test P = 2e-40),
HER2-enriched (Mann–Whitney Wilcoxon test P = 3e-18) and Luminal B
(Mann–Whitney Wilcoxon test P = 9e-50) breast cancers. However,
patients with lower BRCA scores were more likely to be Luminal A
(Mann–Whitney Wilcoxon test P = 8e-91) and Normal-like (Mann–Whitney
Wilcoxon test P = 4e-49) breast cancer. Additionally, normal-like
breast cancer patients tend to have the lowest BRCA scores.
After demonstrating that BRCA scores vary across samples with different
clinical features, we evaluated whether BRCA scores could predict
breast cancer prognosis. Using BRCA score as the independent variable,
we performed survival analyses in the METABRIC discovery and validation
datasets. Namely we divided patients into high and low BRCAness
categories by stratifying on median BRCA score. We observed that high
BRCAness was associated with a significant increase in mortality risk
in the discovery cohort (Fig. [116]4d, P = 2e-06) with a hazard ratio
(HR) of 1.83. Similarly, this result was reproduced in the METABRIC
validation dataset (Fig. [117]4e, P = 6e-07, HR = 1.92). In addition,
we found that the BRCA score was predictive of prognosis in
ER+ patients in both the discovery (HR = 1.63, P = 0.001) and
validation (HR = 2.24, P = 1e-06) datasets, with BRCA score being
associated with increased mortality risk (Supplementary Fig. [118]S3).
In ER− patients, exhibited lower prognostic ability potentially due to
high heterogeneity of ER− tumors^[119]42. Interestingly, we found that
TNBC patients with high BRCA scores exhibited improved survival in the
discovery dataset (Supplementary Fig. [120]S4, HR = 2.21, P = 0.008).
To further evaluate the reproducibility of the BRCA score to predict
survival, we applied the weight profiles to interrogate and perform
survival analysis in two additional breast cancer datasets by Ur-Rehman
et al.^[121]43 and Vijver et al.^[122]44. Our results remained
consistent in that patients with high BRCA scores had worst prognosis
than those with low BRCA scores (Ur-Rehman, Fig. [123]4f, P = 2e-06,
HR = 1.68; Vijver, Fig. [124]4g, P = 2e-05, HR = 3.09). Additionally,
the BRCA score was able to better predict mortality in patients with
ER+ tumors compared to patients with ER− tumors in both datasets
(Supplementary Fig. [125]S4). Furthermore, to determine if the BRCA
score could provide additional prognostic information to traditional
clinicopathological variables, we fitted a multivariate Cox regression
model to the METABRIC dataset using BRCA scores and clinical variables
including age, ER status, Her2 status, stage, and grade as independent
variables. Our results show that BRCA scores (HR = 1.63, P = 3e-03)
remain predictive to prognosis even after considering other clinical
information (Supplementary Table [126]S2).
BRCA score predicts patient response to neoadjuvant chemotherapy
Since BRCAness is a biomarker for responsiveness of
chemotherapy^[127]15, we further examined whether the BRCA score could
predict patient response to neoadjuvant chemotherapy. We calculated
BRCA scores for patients in the Hatzis breast cancer dataset, which
includes treatment response information to neoadjuvant
taxane-anthracycline chemotherapy for 508 breast cancer
samples^[128]45. BRCA scores were calculated with the BRCA1-, BRCA2-
and BRCA1/2-based profiles, respectively. First, we used BRCA scores to
classify patients achieving pathologic complete response (pCR) and
those with residual disease (RD). The results showed that using the
BRCA1-based profile to calculate BRCA scores (AUC = 0.74) achieves the
best accuracy for classification compared to using the BRCA2-
(AUC = 0.59) or BRCA1/2-based (0.66) profiles (Fig. [129]5a). Moreover,
to demonstrate that the BRCA score contributes to clinicopathological
variables in predicting treatment response, we constructed random
forest models to classify pCR patients from patients with RD
(Fig. [130]5b). For one model, we only used the clinical variables
including age, ER status, PR status, HER2 status, grade, stage, node
information as predictors. We compared this model to a second model
where we include both BRCA scores and clinicopathological variables as
predictors. We observed that BRCA scores calculated from the
BRCA1-based profile yielded the highest average AUC (AUC = 0.73)
compared to scores generated from there BRCA2- (AUC = 0.715) and
BRCA1/2-based (AUC = 0.719) profiles. These results suggest that using
the BRCA scores calculated using BRCA1-based BRCAness profiles achieve
the best accuracy for classification which in line with the previously
studies^[131]46,[132]47. Therefore, we applied those BRCA1-based BRCA
scores to the rest chemotherapy prediction.
Figure 5.
[133]Figure 5
[134]Open in a new tab
BRCA score predicts chemotherapy for breast cancer samples. (a) ROC
curves for the accuracy of classifying pathologic complete response
(pCR) from residual disease (RD) breast cancer patients. The BRCA
scores were calculated using gene expression of germline mutate-BRCA1
(red), germline mutate-BRCA2 (blue), germline mutate-BRCA1/2 (green) as
reference to compare with those of sporadic ones, respectively. AUC
scores were listed. (b) Barplot for the mean AUC scores of 10-fold
cross validation using clinical information (gray), clinical
information + BRCA1 based BRCA scores (C+B1, red), clinical
information + BRCA2 based BRCA scores (C+B2, blue) and clinical
information + BRCA1/2 based BRCA scores (C+B1/2, green) to classify pCR
from RD patients. The corresponding average AUCs were listed above each
bar. Standard deviations were plotted with the error bars. (c) Boxplot
for BRCA scores in patients with pCR (dark orange) and RD (chardonnay).
BRCA scores were calculated using the BRCA1-based profiles.
Mann-Whitney-Wilcoxon Test P-value was listed. (d) Barplot for pCR
patients’ fractions in different BRCA score groups. The chardonnay bar
is RD patients and the dark orange bar is pCR patients. Corresponding
pCR rate of each group were listed above the bars.
Moreover, applying the BRCA scores calculated with BRCA1-based BRCAness
profile, we found that pCR patients have higher BRCA scores than RD
patients (Fig. [135]5c, Mann-Whitney-Wilcoxon Test P = 3e-06). In
addition, we observed consistent results in different breast cancer
subtypes. For patients with ER+ (Mann-Whitney-Wilcoxon Test P = 0.002),
ER− (Mann-Whitney-Wilcoxon Test P = 0.002) and TNBC
(Mann-Whitney-Wilcoxon Test P = 0.02), we entirely found that pCR
patients have higher BRCA scores compared to the RD patients
(Supplementary Fig. [136]S5). Moreover, we divided patients into low,
intermediate, and high BRCA score groups and tested the fraction of pCR
patients in each group. As shown in Fig. [137]5d, there were 5.9%,
14.7% and 35.3% pCR patients in these 3 groups. Moreover, patients with
the high BRCA scores were 6-fold more likely to be pCR compared to
those with the low scores. These observations suggest that our BRCA
score could be utilized as a biomarker which can predict the response
to neoadjuvant chemotherapy. Therefore, BRCA score could apply to
practical clinical application helping doctors to improve treatment
decisions and prognosis determinations.
Apply the BRCAness characteristic profile to ovarian cancer
Previous studies have shown that BRCAness also plays an important role
in ovarian cancer^[138]48,[139]49. Briefly, due to the sensitivity of
chemotherapy^[140]50, BRCAness is significantly associated with ovarian
cancer outcome^[141]51. Thus, we evaluated whether the BRCAness
characteristic profile generated using breast cancer profiles can be
applied to interrogate pathway activity in ovarian cancer. Using gene
expression profiles from ovarian cancer tumor samples provided by
Jazaeri et al.^[142]52, we first calculated sample-specific BRCA scores
for each patient in the dataset using the BRCA1/2-based breast cancer
profile. The Jazaeri dataset^[143]52 contained 18 germline BRCA1
mutated, 16 germline BRCA2 mutated and 27 sporadic ovarian cancer
samples. By comparing the BRCA scores, we found that patients with
BRCA1 germline mutations have higher BRCA scores than either patients
with BRCA2 germline mutations (Fig. [144]6a, Mann-Whitney-Wilcoxon Test
P = 0.001) or sporadic ones (Mann-Whitney-Wilcoxon Test P = 7e-04)
which are consistent with their results^[145]52. Moreover, using the
BRCA score could classify BRCA1-mutated familial ovarian cancer tumors
from sporadic ones with high accuracy (AUC = 0.778). When classifying
BRCA1 or BRCA2 mutant samples from sporadic cancers we achieved an
AUC = 0.660; however, classification of only germline BRCA2 mutant
tumors was inaccurate (AUC = 0.528) (Fig. [146]6b). These observations
suggest that the BRCA score using the BRCA1 profile can be applied as a
prognostic biomarker in ovarian cancer.
Figure 6.
[147]Figure 6
[148]Open in a new tab
BRCA scores implements in ovarian cancer. (a) Boxplot for BRCA scores
in germline mutate-BRCA1, germline mutate-BRCA2 and sporadic ovarian
cancer samples. Patients carrying germline BRCA1 mutations had much
higher BRCA scores than sporadic ones (Mann-Whitney-Wilcoxon Test
P = 7e-04). (b) ROC curves for the accuracy of classifying familial
from sporadic ovarian cancer patients using BRCA scores. Black curve:
comparison of patients with germline BRCA1 mutations to sporadic ones.
Magenta curve: comparison of patients with germline BRCA2 mutations to
sporadic ones. Cyan curve: comparison of patients with germline BRCA1
and BRCA2 mutations to sporadic ones. AUC scores were shown. (c)
Kaplan-Meier plots for patients’ prognosis in the Bonome ovarian cancer
dataset. BRCA-like patients (red curve) had better survival than non
BRCA-like ones (green curve). Hazard ratio (HR) and log-rank test
P-value were shown. (d) Same as (c) but for TCGA ovarian cancer
samples.
Therefore, we tested whether the BRCA score could predict ovarian
cancer prognosis using the Bonome et al.^[149]53, TCGA^[150]54 and the
Yoshihara et al.^[151]55 datasets, which contain 185, 557, and 260
patients, respectively. After calculating a BRCA score for each
patient, we divided them into BRCA-like and non BRCA-like groups using
median BRCA score as the cutoff. Interestingly, we observed that
BRCA-like patients exhibited decreased mortality risk in both the
Bomone (Fig. [152]6c, HR = 0.67, P = 0.03) and TCGA datasets
(Fig. [153]6d, HR = 0.68, P = 9e-04). A similar trend was observed
using the Yoshihara dataset (Supplementary Fig. [154]S6, HR = 0.9) but
the difference between two groups was not significant (P > 0.05). These
results were in line with the previous study^[155]28,[156]56, which
suggest the BRCAness profile calculated with breast cancer expression
profiles can be applied to ovarian cancer.
Discussion
Our analyses indicate that BRCAness can be caused by different genomic
mechanisms including somatic mutations, aberrant methylation, deletions
and downregulated expression of genes involved in DNA repair. It is
notable that gene deletions might contribute more to BRCAness than
other genomic changes in sporadic breast cancers (Fig. [157]3a). In
line with this observation, we found that the BRCA score of samples is
more correlated with CNV burden than with mutation burden
(Fig. [158]3b,c). However, this observation may not justify the use of
CNV-based supervised classification models to assess BRCAness^[159]27.
Although BRCAness is associated with a high level of genome instability
as manifested by high CNVs, genomic deletions or amplifications that
occur may not exist in most BRCA-like samples to provide informative
predictors for these classification models due to the heterogeneity of
sporadic tumor samples–BRCAness can be caused by inactivating different
genes via distinct mechanisms, such as DNA methylation and mutations.
To address this shortcoming, we developed a framework that measures
BRCAness based on transcriptomic profiles which capture the final
downstream readout of genomic lesions that abrogate HR pathway
activity. As such, a key question is why can the BRCAness profile be
defined by comparing BRCA1/2-mutant familial tumor samples with
sporadic samples? The familial breast tumor samples used for defining
the BRCAness profile inherit a copy of defective BRCA1/2 gene,
predispose them to breast cancer when the function of the other allele
is lost (again, this can be caused by different genomic mechanisms).
Thus, the familial breast tumor samples carrying BRCA1/2 germline
mutations represent a ‘homogenous’ set of cancers that are driven by HR
pathway inactivation. As a result, it is possible to create a
characteristic BRCAness gene expression profile that encodes the
transcriptomic changes accoiated with known loss of HR pathway
activity. Breast tumor’s HR pathway activity could be inferred via this
profile where a high BRCA score that indicates low activity. Although
the familial breast tumor samples provide an ideal positive dataset for
defining BRCAness profile, the sporadic samples may not be a good
negative dataset since some of these sporadic samples may be BRCA-like
causing by other mechanisms such as BRCA1 promoter DNA methylation,
deletions of other HR genes or post-transcriptional regulation. Thus,
we would expect to further improve our analysis by excluding these
BRCA-like samples to achieve a more accurate BRCAness profile.
In the TCGA breast cancer data, 13 and 14 samples contain at least one
somatic mutation in BRCA1 and BRCA2, respectively. We mapped these
mutations to the protein sequence of BRCA1 and BRCA2, and calculated
the corresponding BRCA scores for these samples (Supplementary
Fig. [160]S7). The majority of these patients with BRCA1/2 somatic
mutations are associated with positive BRCA scores, indicating lower HR
pathway activities. However, nine out of 27 samples are associated with
negative scores, suggesting that different mutation types in BRCA1 and
BRCA2 genes vary in their functional impacts. To inactivate the HR
pathway, both BRCA1/2 alleles have to be defective, which might occur
through different mechanisms. Furthermore, it is often difficult to
determine the effect of a particular mutation occurred in BRCA1/2
genes. Potentially, some mutated BRCA1/2 proteins may preserve the
ability to bind but cannot repair DNA, and furthermore, prevent the
wild-type BRCA1/2 (encoded by the other allele) from carrying out
repairing functions, and therefore result in a dominant
effect^[161]57,[162]58 posing another layer of complexity. Therefore,
the BRCA scores calculated by our framework provide a useful
measurement for BRCAness in sporadic breast cancer samples.
Our analyses indicate that the BRCAness profile defined based on
familial breast cancer samples can be applied to classify
BRCA1/2-mutant familial versus sporadic ovarian cancer. It can also be
used to assess BRCAness in sporadic ovarian tumor samples that show
significant correlation with prognosis of patients. This indicates
shared gene expression patterns between breast and ovarian cancer with
BRCAness phenotypes. Interestingly, BRCAness is associated with poor
prognosis in breast cancer but good prognosis in ovarian cancer. This
might be due to the difference of treatments for these two cancer
types. For ovarian cancers, it has higher likelihood to be high-grade
serous carcinoma (HGSC) when the cancer was investigated^[163]59. It
has been confirmed that HGSC is sensitive to platinum-based
chemotherapy^[164]60. This is consistent with our conclusion that a
patient with high BRCA score (high BRCA score is similar to HGSC) is
more responsive to chemotherapy (Fig. [165]5c,d). In contrast, breast
cancer is mostly driven by ER^[166]61 which is generally treated by
hormone therapy.
Methods
Datasets
We collected gene expression and clinical information for breast cancer
and ovarian cancer patients from 13 datasets (Supplementary
Table [167]S3). Larsen et al.^[168]32 provided the gene expression for
55 familial and 128 sporadic breast tumor samples. In the familial
samples, 33 and 22 carry BRCA1 and BRCA2 germline mutations. We
downloaded this data from the Gene Expression Omnibus (GEO) database
with accession number [169]GSE40115. The [170]GSE50567^[171]33
contained 12 BRCA1- and 1 BRCA2-mutated hereditary breast tumors, 8
BRCAx (non-BRCA1/2 mutations) hereditary breast tumors, 14 sporadic
breast cancer samples and 6 normal samples. The
[172]GSE27830^[173]35–[174]37 dataset provided gene expression profiles
for 155 familial primary breast cancer samples including 47 BRCA1-, 6
BRCA2-, 26 CHEK2- mutant samples and 76 samples without mutations in
these three genes. The [175]GSE19177 dataset^[176]38,[177]39 contained
expression profiles for 19 BRCA1, 30 BRCA2 and 25 non-BRCA1/2 mutation
familial breast cancer samples. The METABRIC breast cancer
dataset^[178]41 contained gene expression and exhaustive clinical
profiles for 1,992 tumors which was downloaded from the European Genome
Phenome Archive with accession number EGAS00000000083. The METABRIC
dataset provided TP53 status for 820 patients including 99 patients
with TP53 mutations and 721 patients with wild type TP53. The Ur-Rehman
dataset^[179]43 contained 1,170 samples integrated from 5 existed
breast cancer datasets^[180]62–[181]66 and was downloaded with
accession number [182]GSE47561. The Vijver dataset^[183]44 was
downloaded from the Netherlands Cancer Institute
([184]http://ccb.nki.nl/data/) and contained gene expression and
clinical profiles for 295 breast cancer patients. The Hatzis
dataset^[185]45 contained the response to neoadjuvant chemotherapy for
508 HER2-negative breast cancer samples with an accession ID
[186]GSE25066. Additionally, we applied our analyses to three OV
datasets. Jazaeri dataset^[187]52 contained 18 germline mutated BRCA1,
16 germline mutated BRCA2 and 27 sporadic ovarian cancer samples
accessing with [188]GSE82007. Bonome et al.^[189]53 provided the gene
expression profile for 185 late-stage and high-grade ovarian cancer
patients with the accession number [190]GSE26712. Yoshihara et
al.^[191]55 contained 260 Japanese advanced-stage ovarian cancer
patients which was downloaded with an accession number [192]GSE32062.
Besides, additional TCGA datasets for breast cancer^[193]67 and ovarian
cancer^[194]54 samples were collected from FIREHOSE Broad institute
([195]https://gdac.broadinstitute.org/).
Define characteristic profiles for homologous recombination DNA repair
pathway
The gene expression dataset generated by Larsen et al.^[196]32 was used
to define the characteristic profile for BRCAness in breast cancer.
This data was provided as log transformed expression at probeset level.
We converted the data into gene expression data based on the probeset
annotation. For genes with multiple probesets, the probeset with the
highest average hybridization signals across all samples was selected
to represent a gene.
Following that a logistic linear regression model was constructed for
each gene to evaluate its power for differentiating BRCA1/2-mutant
samples from sporadic samples while adjusting several important
clinical variables. Specifically, the following model is used:
[MATH: ln(pj/(1−pj))=α+<
/mo>β∗genei
+γ1∗age+γ2∗<
/mo>grade+γ3∗<
/mo>tumorsize+γ<
/mrow>4∗ERstatus+γ5∗<
/mo>HER2status, :MATH]
where p [j] is the probability of the sample j is BRCA1/2-mutant.
For each sample, the model calculated a coefficient and its p-value for
each variable. The sign of β indicates whether gene [i] has higher
expression levels in familial (if β > 0) or sporadic (if β <= 0)
samples. The p-value for β indicates the capability of the gene to
discriminate familial from sporadic samples, the smaller p-value the
more significant discriminative power. Logistic linear regression was
performed for all genes and the resulting beta coefficients were
collected into a vector B [j] = {β [1], β [2],…, β [n]} and the
p-values were collected into a vector P [j] = {p [1], p [2], …, p [n]},
where n is the total number of genes. Based on the two vectors, a pair
of weight profiles was generated to quantify the discriminative power
of all genes to differentiate familial from sporadic samples using the
following functions:
[MATH:
Wj+
=−log10(pi)∗I(βi>0)andWj−=−log10(pi)∗I(βi<=0). :MATH]
This pair of weight profiles characterize up- (W [j] ^+) and
down-regulated (W [j] ^−) genes in j-th familial sample, respectively.
I is the indicator function which outputs 1 if β >= 0 and 0 when
β <= 0. Weights greater than 10 in these profiles were trimmed to avoid
extreme values. By integrating the weight profiles cross samples, we
generated W ^+ and W ^− weight profiles, which, together, defines the
characteristic profile for BRCAness. Based on the Larsen et al.^[197]32
dataset, we define three BRCAness profiles for breast cancer by
comparing BRCA1, BRCA2 and BRCA1/2 familial samples with sporadic
samples, respectively.
Calculate sample-specific activity score for homologous recombination DNA
repair pathway
The BRCAness profile weights genes in a sorted tumor gene expression
profile to discriminate HR-defective samples (familial) from
HR-proficient samples (sporadic). Given this profile, we apply a
rank-based method to infer HR pathway activity in tumor samples based
on their expression profiles. Typically, transcriptomic data for tumor
samples are provided as either relative expression or absolute values
dependent the platforms used for generating the data. When two channel
microarray platforms are used, the resulting data represent relative
expression of genes with respect to a reference sample. In contrast,
one channel microarray and RNA-seq platforms generate absolute
expression level of genes. In this case, we convert data into relative
expression via median normalization.
Providing the relative gene expression profile for a tumor sample, we
rank genes based on their expression and summarize the baseline
expression of genes by referring to their weights in the BRCAness
profile, resulting in a BRCA score which is a quantitative measure of
HR pathway activity. The underlying rationale is that in BRCA-like
samples highly expressed genes (i.e., those with greater positive or
negative relative expression values) tend to have higher weights in the
BRCAness profile (W ^+ and W ^−), while the opposite is true for the
non-BRCA-like samples. This form of correlation is nonlinear and is
sensitive to genes distributed at the two ends of the sorted tumor gene
expression profile. A rank-based statistical algorithm called
BASE^[198]31, which is designed specifically to measure this
correlation pattern, is applied to calculate BRCA scores in tumor
samples. Briefly, genes are sorted into a ranked gene list based on
their relative expression, and the biased distribution of
BRCAness-upregulated (with high values in W ^+) and downregulated (with
high values in W ^−) genes in this list were examined to obtain two
scores, BS ^+ and BS ^−, respectively. The BRCA score is defined as
their difference, BS ^+ − BS ^−. A higher BRCA score indicates high
likelihood of BRCAness and thereby lower HR pathway activity.
Conversely, a lower BRCA score indicates less likelihood of BRCAness
and thereby higher HR pathway activity. A similar statistical framework
has been applied to integrate cancer gene expression data with gene
knockdown profiles with detailed description available from Wang et
al.^[199]30. The calculated BRCA scores were highly consistent with
each other using BRCA1-, BRCA2- and BRCA1/2-based BRCAness profiles in
all datasets we applied in our analyses (Supplementary Fig. [200]S8).
Pathway enrichment analysis
According to the weights in the BRCAness profiles generated by
comparing familial versus sporadic breast cancer samples, we selected
300 genes with the highest positive weights as the genes up-regulated
in familial tumors. Similarly, three hundred down-regulated genes in
familial tumors were selected with the highest negative weights.
Pathway enrichment analysis was performed based on the REACTOME
database^[201]68. Hypergeometric test was used to calculate the
p-value. Adjusted p-value was calculated by Benjamini and Hochberg
method. All analyses were performed in R.
Associate BRCA scores with patient prognosis in breast and ovarian cancer
We applied Kaplan-Meier method to compare the survival times of
patients in different groups. A log-rank test P-value was calculated to
estimate the difference. Median of BRCA scores was applied as a
threshold to divide patients into low and high BRCA score groups. The
Cox proportional regression model was used to evaluate the individual
contribution of BRCA scores to predicting survival in addition to the
other clinical variables including age, ER status, Her2 status, tumor
stage and tumor grade. Survival analysis was performed with the R
package “survival”.
Correlate BRCA scores with genomic features
Using the gene expression profiles of TCGA breast cancer samples, we
calculated a BRCA score for each sample. Because of the calculated BRCA
score infers HR pathway activity, we focused on the genomic features of
27 HR genes that explains the majority of the correlation between BRCA
score and genomic features including CNV, DNA methylation, and gene
expression. First, we ranked TCGA breast cancer patients based on their
BRCA scores in decreasing order. For CNV and gene expression, we
compared the difference between that in top ranked (from 1% to 20%)
patients and the remaining ones for each 27 HR genes. For DNA
methylation profile, we first used the average beta value of CpGs in
the promoter (from −2k to 2k of transcription start site) of a gene to
represent this gene. Then, we compared the difference between that in
top ranked (from 1% to 20%) patients and the remaining ones for each 27
HR genes. The Mann-Whitney-Wilcoxon Test was applied to calculate the
difference. The calculations of CVN burden and mutation burden were
same with our previously study^[202]30.
Associate BRCA scores with patient responsiveness to neoadjuvant chemotherapy
We used random forest^[203]69 models to predict pCR and RD patients in
the Hatzis dataset. We only considered clinical variables such as age,
ER status, PR status, HER2 status, tumor grade, tumor stage, node
information as predictors in the one model. In the other models, we
combined the BRCA scores calculated using BRCA1-, BRCA2- and
BRCA1/2-based profiles with those clinical predictors. The accuracy of
prediction was investigated by calculating the AUC scores with 10-fold
cross validation. The average and standard deviation of AUC scores were
calculated to show the accuracy. The R package “randomForest” was used
to perform these analyses. Moreover, R function “quantile” was utilized
to divide patients into three groups.
Electronic supplementary material
[204]Supplementary files^ (11.4MB, pdf)
Acknowledgements