Research

JAMA Oncology | Original Investigation

Prognostic and Predictive Value of Immune-Related Gene Expression
Signatures vs Tumor-Infiltrating Lymphocytes in Early-StageERBB2/HER2-Positive Breast Cancer
A Correlative Analysis of the CALGB 40601 and PAMELA Trials
Aranzazu Fernandez-Martinez, MD, PhD; Tomás Pascual, MD; Baljit Singh, MD; Paolo Nuciforo, MD, PhD;
Naim U. Rashid, PhD; Karla V. Ballman, PhD; Jordan D. Campbell, PhD; Katherine A. Hoadley, PhD;
Patricia A. Spears, BS; Laia Pare, PhD; Fara Brasó-Maristany, PhD; Nuria Chic, MD; Ian Krop, MD, PhD;Ann Partridge, MD; Javier Cortés, MD, PhD; Antonio Llombart-Cussac, MD, PhD; Aleix Prat, MD, PhD;Charles M. Perou, PhD; Lisa A. Carey, MD
Supplemental content
IMPORTANCE Both tumor-infiltrating lymphocytes (TILs) assessment and immune-related
gene expression signatures by RNA profiling predict higher pathologic complete response(pCR) and improved event-free survival (EFS) in patients with early-stage ERBB2/HER2positive breast cancer. However, whether these 2 measures of immune activation provide
similar or additive prognostic value is not known.
OBJECTIVE To examine the prognostic ability of TILs and immune-related gene expression

signatures, alone and in combination, to predict pCR and EFS in patients with early-stage
ERBB2/HER2-positive breast cancer treated in 2 clinical trials.
DESIGN, SETTING, AND PARTICIPANTS In this prognostic study, a correlative analysis was
performed on the Cancer and Leukemia Group B (CALGB) 40601 trial and the PAMELA trial.
In the CALGB 40601 trial, 305 patients were randomly assigned to weekly paclitaxel withtrastuzumab, lapatinib, or both for 16 weeks. The primary end point was pCR, with asecondary end point of EFS. In the PAMELA trial, 151 patients received neoadjuvant treatment
with trastuzumab and lapatinib for 18 weeks. The primary end point was the ability of the
HER2-enriched subtype to predict pCR. The studies were conducted from October 2013 toNovember 2015 (PAMELA) and from December 2008 to February 2012 (CALGB 40601). Data
analyses were performed from June 1, 2020, to January 1, 2022.
MAIN OUTCOMES AND MEASURES Immune-related gene expression profiling by RNA
sequencing and TILs were assessed on 230 CALGB 40601 trial pretreatment tumors and 138
PAMELA trial pretreatment tumors. The association of these biomarkers with pCR (CALGB
40601 and PAMELA) and EFS (CALGB 40601) was studied by logistic regression and Cox
analyses.
RESULTS The median age of the patients was 50 years (IQR, 42-50 years), and 305 (100%)
were women. Of 202 immune signatures tested, 166 (82.2%) were significantly correlatedwith TILs. In both trials combined, TILs were significantly associated with pCR (odds ratio,
1.01; 95% CI, 1.01-1.02; P = .02). In addition to TILs, 36 immune signatures were significantly
associated with higher pCR rates. Seven of these signatures outperformed TILs for predicting
pCR, 6 of which were B-cell related. In a multivariable Cox model adjusted forclinicopathologic factors, including PAM50 intrinsic tumor subtype, the immunoglobulin G
signature, but not TILs, was independently associated with EFS (immunoglobulin G
signature–adjusted hazard ratio, 0.63; 95% CI, 0.42-0.93; P = .02; TIL-adjusted hazard ratio,
1.00; 95% CI, 0.98-1.02; P = .99).
CONCLUSIONS AND RELEVANCE Results of this study suggest that multiple B-cell–related
signatures were more strongly associated with pCR and EFS than TILs, which largely
represent T cells. When both TILs and gene expression are available, the prognostic value of
immune-related signatures appears to be superior.

JAMA Oncol. doi:10.1001/jamaoncol.2022.6288
Published online January 5, 2023.

Author Affiliations: Author
affiliations are listed at the end of thisarticle.
Corresponding Author: Lisa A.
Carey, MD, Lineberger
Comprehensive Cancer Center,
University of North Carolina at Chapel
Hill, 450 West Dr, CB7295, Chapel
Hill, NC 27599-7295 (lisa_carey@
med.unc.edu).

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Research Original Investigation

Immune-Related Gene Expression Signatures vs Tumor-Infiltrating Lymphocytes in ERBB2/HER2-Positive Breast CancerDuring the last 2 decades, the outcome of patients withearly-stage ERBB2/HER2-positive breast cancer has
markedly improved owing to new treatment strategies combining polychemotherapy and multiple ERBB2/HER2targeted drugs.1-10 However, it is increasingly evident that many
patients are overtreated by the recommended regimens,
whereas others still experience metastatic relapse. A primaryresearch focus in breast cancer is to better tailor treatments torisk; to accomplish this, effective prognostic and predictive
biomarkers are needed.
Increasing evidence suggests that the activation of the hostimmune system mediates the response to ERBB2/HER2targeted therapies in breast cancer.11 Currently, there are several methods to assess intratumor immune activation. The
presence of tumor-infiltrating lymphocytes (TILs) in thehematoxylin-eosin–stained tumor slides is one of these methods, and an international working group has established standardized tools for measuring TILs.12 The percentage of TILs that
infiltrate the breast tumor is positively prognostic in patientswith early-stage ERBB2/HER2-positive breast cancer treatedwith anti-ERBB2/HER2 therapies in multiple scenarios: in the
neoadjuvant and adjuvant setting, in the presence or absenceof chemotherapy, with single and dual ERBB2/HER2 blockade, and when assessed at baseline and during treatment.13-18
Other than TILs, immune activation can also be measured bygene expression.19,20 In patients with early-stage ERBB2/HER2positive breast cancer treated in the neoadjuvant setting, immune-related gene expression signatures (iGESs) are associated with higher pathologic complete response (pCR) rates andprolonged survival.21-23 Specifically, the immunoglobulin G(IgG) signature24 has previously shown strong and independent prognostic value across many studies.2,19,22,25 However,the comparative prognostic ability of these different means ofmeasuring immune activation has not been well examined. In
this retrospective predictive and prognostic study, we tested
which biomarker, or combination of biomarkers, is the mostpowerful for response and survival in 2 independent clinical
trials: the Cancer and Leukemia Group B (CALGB) 40601 trial(NCT00770809) and the PAMELA trial (NCT01973660),
respectively. The CALGB is now part of the Alliance for Clinical
Trials in Oncology.

Methods
Neoadjuvant Trials
The CALGB 40601 trial study design, pCR, event-free survival (EFS), overall survival, and genomic correlative studies
have been previously published.2,22 In this predictive and prognostic study, a total of 305 women with stage II to III ERBB2/
HER2-positive breast cancer were randomly assigned to receive neoadjuvant weekly paclitaxel with the addition of
trastuzumab, lapatinib, or both for 16 weeks. The primary end
point was pCR, defined as no invasive tumor in the breast at
surgery, and secondary end points included EFS. The
PAMELA trial study design, pCR, and biomarker correlative
studies have also been previously published.14,26,27 In thisphase 2 trial, 151 patients with stage I to IIIA ERBB2/HER2E2

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Key Points
Question Which immune-related biomarker provides the mostvaluable information to predict pathologic complete response andevent-free survival in patients with early-stage ERBB2/HER2positive breast cancer: tumor-infiltrating lymphocytes,
immune-related gene expression signatures, or both?
Findings In this predictive prognostic study in which a combinedcorrelative analysis of the CALGB 40601 and PAMELA trials was
conducted, 305 patients with early-stage ERBB2/HER2-positive
breast cancer, 6 B-cell–related signatures were more strongly
associated with pathologic complete response than weretumor-infiltrating lymphocytes. In a multivariable Cox model
performed in the CALGB 40601 trial, the immunoglobulin G
signature, but not tumor-infiltrating lymphocytes, wasindependently associated with event-free survival.
Meaning Findings suggest that when both tumor-infiltrating
lymphocytes and gene expression are available, the prognostic
and predictive value of RNA sequencing–based immune signatures
is superior.

positive breast cancer received neoadjuvant lapatinib plus
trastuzumab for 18 weeks. The primary outcome was the ability of the HER2-enriched subtype to predict pCR, defined as
no invasive tumor in the breast at surgery. Each trial participant signed an institutional review board–approved (National Cancer Institute Central institutional review board for
the CALGB 40601 trial and Hospital Universitari Vall d’Hebron
for the PAMELA trial), protocol-specific informed consent
document following federal and institutional guidelines.

Tumor Gene Expression Analyses and iGESs
Gene expression profiles from pretreatment core biopsies wereobtained from 264 of 305 CALGB 40601 trial participants(86.6%) and 142 of 151 PAMELA trial participants (94.0%) (eFigure 1 in the Supplement). Whole-transcriptome analyses bymessenger RNA sequencing (RNA-Seq) were performed in the
University of North Carolina High-Throughput Sequencing
Facility and analyzed by the university’s Lineberger Comprehensive Cancer Center Bioinformatics Core. The RNA sequencing libraries were made from total RNA with the TruSeq (Illumina) messenger RNA kit in the CALGB 40601 trial and the
TruSeq RNA Access kit in the PAMELA trial and were sequenced on an Illumina HiSeq 2000 using a 2 × 50–base pairconfiguration. The CALGB 40601 trial RNA-Seq FASTQ files areavailable on the dbGAP repository (phs001570.v3.p1). The
PAMELA trial RNA-Seq FASTQ files are available on EGA(EGAS00001006410/EGAD00001009054). Purity-filteredreads were aligned to the human reference GRCh38/hg38
genome, using Spliced Transcripts Aligned to a Reference,version 2.4.2a.28 Transcript (GENCODE, version 22) abundance
estimates were generated by Salmon, version 0.6.0 29 in“-quant” mode, based on the Spliced Transcripts Aligned to a
Reference alignments. Raw read counts for all RNA-Seq samples
were normalized to a fixed upper quartile. 30 Messenger
RNA sequencing–normalized gene counts were then log2transformed, and genes were filtered for those expressed in
70% of samples. The batch effect between the gene expression
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Immune-Related Gene Expression Signatures vs Tumor-Infiltrating Lymphocytes in ERBB2/HER2-Positive Breast Cancerfrom the CALGB 40601 and PAMELA trials was corrected byapplying the distance-weighted discrimination method,31,32
version 1.0.233 (SlicerSALT) and using the CALGB 40601 trial
as reference. Intrinsic subtypes were obtained from RNA-Seq
gene expression data as described elsewhere.22
Expression of 202 iGESs from 43 publications (eReferences in the Supplement) was calculated. The list of iGESs andthe genes within each signature are summarized in eTable 1
in the Supplement, and the R code is provided.34 Finally, theiGESs were classified into 22 immune classes based on theirgene ontology, with CIBERSORT as reference.35

TIL Evaluation
In the CALGB 40601 and PAMELA trials, slides from core biopsies were available for 230 of 264 patients (87.1%) and 138 of 142
patients (97.2%) from the RNA-Seq cohort (eFigure 1 in the
Supplement), respectively. The stromal TILs from both clinical trials were scored by the CALGB 40601 trial lead study pathologist (B.S.), following the International TILs Working Group
recommendations.12 In patients with more than 1 core biopsy
available, the mean of the 2 TILs assessments was calculated,and patients without TILs assessment were censored.

Original Investigation Research(IQR, 8.10-9.84). The association of immune biomarkers with
EFS was evaluated with Cox regression models. P values were
adjusted for multiple testing with a Benjamini-Hochberg correction. To compare the goodness of fit, we used the AIC. To
evaluate the accuracy, we calculated an average C index using
5-fold cross-validation. To avoid a potential guarantee time bias
in the multivariable EFS models including pCR status, we performed a 30-week landmark analysis. The landmark subpopulation included only patients without events who were followed up at 30 weeks after randomization.38,39 Finally, tocompare the prognostic ability of 2 nested models, we used thelikelihood ratio test (LRT).
All the analyses were based on the study clinical database frozen on June 10, 2021. All tests were 2-sided, and a .05
level of significance was used. All statistical analyses were performed with R version 3.5.2 (R Foundation for StatisticalComputing) and Python version 3.6 (Python Software Foundation). Data analyses were performed from June 1, 2020, toJanuary 1, 2022.

Results

Statistical Analysis

Baseline Patient Characteristics and TIL Distribution

The criteria of the Reporting Recommendations for TumourMarker Prognostic Studies (REMARK) guidelines were followed for this study.36 Comparisons of differences in baseline
clinicopathologic variables between the CALGB 40601 trial and
the PAMELA trial were made with the Wilcoxon rank sum test(continuous variables) and the χ2 test (categorical variables).
For pCR and EFS modeling, the iGES scores were analyzed as continuous variables. Stromal TILs were analyzed ascontinuous and discrete variables with different prespecified
cutoffs (ie, 20%, 40%, and 60%). Immune-related gene expression signatures and TILs were also categorized by tertiles(ie, low, medium, and high) for visualization purposes.
The association between TILs and iGESs was measured
with Spearman correlation coefficients. For differential gene
expression analysis, we performed a multiclass significanceanalysis of microarrays.37
The association of immune biomarkers with pCR was
evaluated in the CALGB 40601 and PAMELA combined cohort by logistic regression models. P values were adjusted for
multiple testing with a Benjamini-Hochberg method to control the false discovery rate. To compare the goodness of fit of
2 models, we used the Akaike information criterion (AIC). As
accuracy metric, we calculated the area under the receiver operating characteristic curve (AUC) for pCR, using the CALGB
40601 trial as a train set and the PAMELA trial as validation.
In the CALGB 40601 trial, a mean of the AUC was calculatedwith 10-fold cross-validation.
In the CALGB 40601 trial, EFS was defined as the time fromrandomization to a breast cancer relapse after surgery, second primary malignant neoplasm, or death without recurrence for women who underwent surgery. For individuals who
did not undergo surgery, the event was defined as death during clinical follow-up or noncompletion of neoadjuvant therapy
due to progressive disease. The median follow-up was 9.1 years

The characteristics of the 305 patients included in the study
are summarized in Table 1. Data on race and ethnicity were collected in the CALGB 40601 trial but not the PAMELA trial;
therefore, we decided not to include the information in thisstudy. The median age of the patients was 50 years (IQR, 42-50
years), and 305 (100%) were women. Patients enrolled in the
CALGB 40601 trial were significantly younger, more likely tobe premenopausal, and at a more advanced clinical stage at
diagnosis than those enrolled in the nonchemotherapy
PAMELA trial. There were no significant differences between
the trials in the hormone receptor status and intrinsic subtype distribution. In the CALGB 40601 trial, there were no statistically significant differences in the baseline clinicopathologic characteristics between the TIL cohort (n = 230) and thelandmark cohort (n = 227) (eTable 2 in the Supplement).
In the CALGB 40601 trial, the median TIL count was 20%(IQR, 13.1%-45%) (eFigure 2A in the Supplement). In the
PAMELA trial, the median TIL count was 30% (IQR, 20%50%) (eFigure 2B in the Supplement); the TILs distribution was
not significantly different between the studies (eFigure 2C in
the Supplement). The proportion of TILs was significantly
higher in hormone receptor–negative compared with hormone receptor–positive disease in the CALGB 40601 trial (median in hormone receptor–negative disease = 30 [IQR, 1560]; median in hormone receptor–positive disease = 20 [IQR,
10-35]; P = .03), the PAMELA trial (median in hormone receptor–negative disease = 30 [IQR, 20-65]; median in hormone receptor–positive disease = 20 [IQR, 10-35]; P = .04), and the
combined cohort (median in hormone receptor–negative disease = 30 [IQR, 19-60]; median in hormone receptor–positive
disease = 20 [IQR, 10-36]; P = .001) (eFigure 3A and C in the
Supplement; Figure 1A). There was a significant differencein the proportion of TILs by tumor intrinsic subtype, with asignificantly higher proportion of TILs in nonluminal (ie,jamaoncology.com

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Immune-Related Gene Expression Signatures vs Tumor-Infiltrating Lymphocytes in ERBB2/HER2-Positive Breast Cancer

Table 1. Baseline Characteristics of Patients From the Study Population by Clinical Trial
Patients, No. (%)
Characteristic

CALGB 40601 trial(n = 230)

PAMELA trial(n = 138)

All(N = 368)

Age, median (IQR), y

49 (41-56)

54 (44-64)

50 (42-59)

Postmenopausal

89 (38.7)

81 (58.7)

170 (46.2)

Premenopausal

141 (61.3)

57 (41.3)

198 (53.8)

Negative

93 (40.4)

67 (48.6)

160 (43.5)

Positive137 (59.6)

71 (51.4)

208 (56.5)

I0

45 (32.6)

45 (12.2)

II

157 (68.3)

86 (62.3)

242 (65.8)

III

73 (31.7)

8 (5.8)81 (22.0)

HL ± ET0138 (100)

138 (37.5)

TH

89 (38.7)

089 (24.2)

THL

95 (41.3)

095 (25.8)

TL

46 (20.0)046 (12.5)

Basal-like

19 (8.3)

7 (5.1)26 (7.1)

HER2-enriched

131 (57.0)

91 (65.9)

222 (60.3)

Luminal A

26 (11.3)

20 (14.5)

46 (12.5)

Luminal B

32 (13.9)

16 (11.6)

48 (13.0)

Normal-like

22 (9.5)

4 (2.9)26 (7.1)

P valuea

Menopause status<.001

Hormone receptor status.13

Clinical stage<.001

Treatment<.001

Abbreviations: CALGB, Cancer and
Leukemia Group B 40601 trial;
ET, endocrine therapy;
HL, trastuzumab plus lapatinib;
TH, weekly paclitaxel plus
trastuzumab; THL, TH plus lapatinib;
TL, weekly paclitaxel plus lapatinib.

.06aIntrinsic subtype

Statistical differences were assessedwith the Wilcoxon rank sum test (forage) and the Pearson χ2 test (for the
rest of the variables).

Figure 1. Comparison of Stromal Tumor-Infiltrating Lymphocyte (TIL) Levels by Hormone Receptor Status (A) and Intrinsic Subtype (B)in the Combined Cohort of the Cancer and Leukemia Group B 40601 Trial and the PAMELA Trial
A Comparison of stromal TIL levels by hormone receptor status

B

P =.001

P =.001100

Comparison of stromal TIL levels by intrinsic subtype
P =.001

100

80

60

60

TILs, %

TILs, %

P =.001
80

40

40

20

20

0

0
Hormone receptor positive

Hormone receptor negative

Hormone receptor status

Basal

HER2-enriched

Luminal B

Luminal A

Intrinsic subtype

Statistical differences were assessed with the Kruskal-Wallis test. The horizontal line in each box plot indicates the median of the distribution.

basal-like and HER2-enriched) compared with luminal tumors (CALGB 40601 trial: median nonluminal tumors = 25
[IQR, 15-60], median luminal tumors = 20 [IQR, 10-30], P = .01;
PAMELA trial: median nonluminal tumors = 30 [IQR, 20-60],
median luminal tumors = 20 [IQR, 10-30], P = .004; combined cohort: median nonluminal tumors = 30 [IQR, 15-60],
median luminal tumors = 20 [IQR, 10-30], P < .001) (eFigure 3B and D in the Supplement; Figure 1B).
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Association Between TILs and iGESs
We found that 166 of 202 iGESs (82.2%) were significantly correlated with TILs in both studies, 179 in the CALGB 40601 trial
and 174 in the PAMELA trial (eTable 3 in the Supplement).
Spearman correlation coefficients are summarized in eTable 3
in the Supplement, and the coefficients from the 20 signatures that were the most correlated with TILs in both studies
are represented in eFigure 4 in the Supplement. The iGESs most
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Immune-Related Gene Expression Signatures vs Tumor-Infiltrating Lymphocytes in ERBB2/HER2-Positive Breast Cancer

Original Investigation Research

Figure 3. Rates of Pathologic Complete Response (pCR) According to Levels of Tumor-Infiltrating Lymphocytes (TILs) (A) and Immunoglobulin G (IgG)
Gene Expression Signature (B)A TILs levelB

100

lgG signature
100

TIL levels
0%-20%≥20%-40%Signature levels>40%-100%Low

80

pCR rate, %pCR rate, %

80

60

40

20

Medium

High

60

40

20

0

0
57

19

19

Patients receiving
THL

47

15

27

Patients receiving
TH

19

14

13

Patients receiving
TL

123 48

59

Patients receiving
HL

Treatment group

32

35

28

Patients receiving
THL

21

32

36

Patients receiving
TH

12

15

19

Patients receiving
TL

58

40

40

Patients receiving
HL

Treatment group

Both variables were divided by tertiles to illustrate their association with pCR. HL indicates trastuzumab plus lapatinib; TH, weekly paclitaxel plus trastuzumab;
THL, TH plus lapatinib; and TL, weekly paclitaxel plus lapatinib.

correlated with TILs were largely T-cell related. The highest
Spearman correlation coefficient for TILs was 0.61 in the CALGB
40601 trial and 0.71 in the PAMELA trial. Three signatures associated with resistance to immunotherapy, inflammation, andimmunosuppression were significantly negatively correlatedwith TILs in both studies (Spearman correlation coefficientsfor the CALGB 40601 and PAMELA trials: −0.29 and −0.37,−0.26 and −0.22, and −0.17 and −0.23, respectively) (eFigure 5 and eTable 3 in the Supplement).
To further study the association between TILs and iGESs,
we compared the differences in immune cell infiltration, using
our CIBERSORT-derived signatures to perform a multiclass significance analysis of microarrays by TIL levels (ie, low, medium, and high) and by IgG signature levels (ie, low, medium,and high) in the CALGB 40601 and PAMELA trials. The standardized mean differences between the iGESs in 1 class vs theoverall mean expression are represented in Figure 2A (theCALGB 40601 trial) and Figure 2B (the PAMELA trial). Tumors with high TIL and IgG levels were significantly enriched
for T cells compared with those with low TIL and IgG levels.
However, although tumors with high IgG levels were enriched for B-cell and plasma cell signatures compared withthose with low levels, in both studies, samples with high andlow TIL levels showed a high expression of B-cell and plasma
cell signatures. This analysis suggests that TILs do not recapitulate B-cell and plasma cell immune infiltration, and the 2biomarkers should not be considered the same.

low levels of TILs using a cutoff of 20% and 40% were alsosignificantly associated with pCR (20%: odds ratio, 1.86;
95% CI, 1.20-2.91; P = .04; 40%: odds ratio, 2.29; 95% CI,
1.40-3.77; P = .02). The model including TILs with a cutoff of
40% rather than TILs as a continuous variable better predicted pCR (AIC, 471.68 for TILs with a cutoff of 40% vs
472.23 for TILs as a continuous variable; AUC, 0.59 for TILs
with a cutoff of 40% in the PAMELA trial vs 0.57 for TILs as acontinuous variable).
Thirty-six of 202 iGESs (17.8%) were also significantly associated with pCR independently of the treatment group andstudy (eTable 4 in the Supplement; Figure 3B; eFigure 6 in the
Supplement). The biomarker models from 13 of these signatures better predicted the response end point than the best TILs
model for pCR prediction, showing lower AIC values (AIC,
450.06-471.30 for iGESs vs 471.68 for TILs with a cutoff of 40%)(eTable 4 in the Supplement). When the accuracy metrics were
examined, 7 iGESs outperformed TILs for pCR, showing higher
AUC values in the train (CALGB 40601 trial) and the validation set (PAMELA trial) (eTable 5 in the Supplement). Moreover, in multivariable models adjusted by multiple clinicalparameters (study, treatment group, stage, age, hormone receptor status, menopausal status, and subtype), these signatures but not TILs were significantly associated with pCR(eTable 5 in the Supplement). Most of these signatures wereassociated with B cells, plasma cells, and immunoglobulins(eFigure 7 in the Supplement).

Association of TILs and iGESs With pCR
in the CALGB 40601 and PAMELA Trials

Association of TILs and iGESs With EFS
in the CALGB 40601 Trial

In the combined cohort, the percentage of TILs as a continuous variable was significantly associated with pCR, with anodds ratio of 1.01 (95% CI, 1.01-1.02; P = .02) for each 1%
increase in TILs. This association was observed regardless ofthe clinical trial and treatment group (Figure 3A). High vsIn the CALGB 40601 trial, 37 iGESs, but not TILs, were significantly associated with EFS in Cox regression models adjusted
by treatment group (eTable 6 in the Supplement). As with pCR,
the top-performing immune signatures were also associatedwith B cells.

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Immune-Related Gene Expression Signatures vs Tumor-Infiltrating Lymphocytes in ERBB2/HER2-Positive Breast Cancer

Figure 2. Heatmap Representing the Different Distributions of the CIBERSORT-Derived Gene Expression Signatures
by Tumor-Infiltrating Lymphocyte (TIL) and Immunoglobulin G (IgG) Levels
A CALGB 40601

TILs

IgG

0.8

B cells (memory)

B cells

B cells (naive)

Plasma cells

0.4

Plasma cells

0

T cells (CD4 memory activated)
T cells (CD8)–0.4

T cells (follicular helper)
T cells (CD4 memory resting)–0.8

Dendritic cells
T cells
NK cells
Neutrophils
Eosinophils

T cells (gamma delta)

Macrophages

T regsMastocytes

T cells (CD4 naive)

Monocytes

NK (activated)
NK (resting)Dendritic cells (activated)
Dendritic cells (resting)Macrophages (M1)
Macrophages (M0)
Macrophages (M2)
Mast cells (resting)
Mast cells (activated)
Monocytes
Neutrophils
Eosinophils
Low
B

MediumHigh

Low

MediumHigh

PAMELA
IgG

TILs

B cells (memory)

0.8

B cells (naive)
0.4

Plasma cells

0

T cells (CD4 memory activated)
T cells (CD8)–0.4

T cells (follicular helper)
T cells (CD4 memory resting)–0.8

T cells (gamma delta)
T regsT cells (CD4 naive)
NK (activated)
NK (resting)Dendritic cells (activated)
Dendritic cells (resting)Macrophages (M1)
Macrophages (M0)
Macrophages (M2)
Mast cells (resting)
Mast cells (activated)
Monocytes
Neutrophils
Eosinophils
Low

MediumHigh

Low

MediumCancer and Leukemia Group B 40601 (CALGB 40601) and PAMELA trial
samples were classified into 3 different groups by study, depending on the TIL
and IgG levels by tertiles (ie, low, medium, and high TILs; and low, medium, andhigh IgG). Then, a multiclass significance analysis of microarrays was performed.

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High

The standardized mean differences between the immune signatures in 1 class vsthe overall mean expression for each study are represented in 4 heatmaps.
NK indicates natural killer; T regs, T regulatory cells.

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Immune-Related Gene Expression Signatures vs Tumor-Infiltrating Lymphocytes in ERBB2/HER2-Positive Breast Cancer

Finally, we wanted to test whether the combination of
iGESs and TILs was more prognostic than each alone by comparing multiple multivariable Cox regression models. To test
this, we selected 6 immune signatures that outperformed TILs
for pCR and were also prognostic in the CALGB 40601 trial: 3
IgG signatures, 2 B-cell signatures, and 1 plasma cell signature. The results for 1 of the IgG signatures are shown in Table 2.
We first built a base model that included known prognostic variables: pCR status, treatment group (weekly paclitaxel plus
trastuzumab plus lapatinib, weekly paclitaxel plus lapatinib,
or weekly paclitaxel plus trastuzumab, where the latter is thereference group), hormone receptor status, clinical stage, andPAM50 intrinsic tumor subtype (HER2-enriched vs other subtypes) (model 1). In this model, treatment group, clinical stage,pCR status, and intrinsic subtype were significantly associated with EFS. Adding TILs (model 2) did not provide additional prognostic information (LRT P = .12). We then built amodel adding our previously published IgG signature to model
1; in this model (model 3), treatment group, clinical stage, pCR
status, intrinsic subtype, and the IgG signature were all significantly associated with EFS (IgG-adjusted hazard ratio, 0.63;
95% CI, 0.45-0.87; P = .006). Also, model 3 was significantly
better than model 1 for EFS (LRT P = .005). The last model(model 4) included both TILs and the IgG signature. In thismodel, the IgG signature, but not TILs, was significantly associated with EFS (IgG-adjusted hazard ratio, 0.63; 95% CI,
0.42-0.93; P = .02; TIL-adjusted hazard ratio, 1.00; 95% CI,
0.98-1.02; P = .99) (Table 2). Model 4, including both TILs andthe IgG signature, was significantly better than the model including only TILs (model 4 vs model 2; LRT P = .02), but it was
not significantly better than model 3, which included only the
IgG signature (model 4 vs model 3; LRT P = .99). Similar results were observed with the other 5 iGESs (eTable 7 in the
Supplement) when TILs was used as a discrete variable witha cutoff of 40% (eTable 8 in the Supplement) and when a landmark analysis was performed (eTable 9 in the Supplement).
When different multivariate models including clinical parameters and 1 iGES were compared, the immunoglobulin–The Cancer Genome Atlas signature model performed slightly better
than the rest (eTable 10 and eTable 11 in the Supplement).

Discussion
In the CALGB 40601 and PAMELA trials, both the proportionof TILs and the multiple iGESs were significantly associatedwith pCR. Moreover, in the CALGB 40601 trial, several immune signatures were also associated with EFS in univariable and multivariable Cox analyses that included clinical factors and intrinsic subtype, whereas TILs were not significantly
associated with outcome. B-cell signatures outperformed
TILs and T-cell signatures for pCR and EFS.40 Combining TILs
and iGESs did not provide additional prognostic information.
These results are especially relevant in early-stage ERBB2/
HER2-positive breast cancer, in which multiple trials
focus on developing prognostic tools combining tumor andimmune cell biomarkers to guide treatment escalation andde-escalation.25,41-43
jamaoncology.com

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Original Investigation Research

The proportion of TILs as a continuous variable has provento be an independent prognostic biomarker in early-stage
ERBB2/HER2-positive breast cancer.13-18 Thus, the evaluation of TILs has been proposed as a readily available tool toidentify different prognostic groups in this setting. Despite thestandardization of TIL scoring by the International TILs Working Group, this biomarker still has low reproducibility rates
across pathologists, with κ values in the 0.4 to 0.6 range44,45
even after efforts to define the optimal TIL cutoff.46 However, these are ongoing harmonization efforts, and TILs havevalue, particularly in resource-constrained settings.
Not surprisingly, quantitative gene expression of
immune-related genes and signatures is strongly associatedwith the infiltration of TILs.47,48 Immune genes and signatures have also proven to have prognostic and predictive
value in patients with early-stage ERBB2/HER2-positive
breast cancer, and the IgG signature is included in the
HER2DX genomic test.21-23,25 To our knowledge, this is thefirst study demonstrating that when both TILs and immune
gene expression data are available, iGESs, particularly B-cellsignatures, provide more prognostic information in ERBB2/
HER2-positive breast cancer without the additional value ofadding TILs. Similar results suggest the potential superiorityof iGESs over TILs in triple-negative breast cancer treatedwith chemotherapy alone in the CALGB 40603 trial,48 inwhich B-cell features, including IgG expression, were foundto be the most prognostically valuable metric.

Limitations
Our study has limitations. First, a substantial proportion ofpatients included in the CALGB 40601 and PAMELA trials received trastuzumab combined with lapatinib, a dual treatment used in the metastatic setting but not approved for earlystage ERBB2/HER2-positive breast cancer. Moreover, the
PAMELA trial differed from the CALGB 40601 trial in that patients received only anti-ERBB2/HER2 therapy before surgery, without chemotherapy. Second, although EFS was a key
secondary end point of the CALGB 40601 trial, the trial was
not powered for long-term outcomes, so EFS prediction modeling should be interpreted with caution. Third, even whenscored as a continuous variable, the proportion of TILs (ie, 0%
to 100%) follows a semiquantitative pattern, with increments of 5% to 10%, and thus is not a true continuous variable like iGESs. However, when TILs were divided into highvs low levels based on multiple prespecified cutoffs, their ability to predict response and survival was lower than that of multiple B-cell–related signatures. Finally, we performed numerous statistical predictions simultaneously by building 1 modelfor each immune biomarker to predict pCR and EFS. To control type I error, we adjusted the models’ P values for multiple testing.

Conclusions
To conclude, accumulating evidence supports the validity ofusing evidence of immune activation, which can be measured with TILs or immune-related gene expression(Reprinted) JAMA Oncology Published online January 5, 2023

E7

Research Original Investigation

Immune-Related Gene Expression Signatures vs Tumor-Infiltrating Lymphocytes in ERBB2/HER2-Positive Breast Cancer

Table 2. Association of TILs and Immune-Related Gene Expression Signatures With EFS in the Cancer
and Leukemia Group B 40601 Trial
Model, formula, and features

HR (95% CI)

P value

THL vs TH

0.34 (0.14-0.78).01

TL vs TH1.27 (0.62-2.60).51

HR (pos vs neg)

1.86 (0.94-3.69).08

Stage (III vs II)

2.03 (1.07-3.87).03

pCR (pCR vs RD)

0.22 (0.10-0.48)<.001

HER2-enriched vs other4.20 (1.97-8.96)

<.001

AIC

LRT P valuea

395.68

1 [Reference]

395.31Model 2 vs model 1
LRT: P = .12

389.96

Model 3 vs model 1
LRT: P = .005

Model 1:EFS ≈ treatment + HR + stage + pCRB + subtype

Model 2:
EFS ≈ treatment + HR + stage + pCRB + subtype + TILs
THL vs TH

0.31 (0.13-0.73).007

TL vs TH1.30 (0.63-2.67).48

HR (pos vs neg)

1.85 (0.93-3.69).08

Stage (III vs II)

1.95 (1.02-3.71).04

pCR (pCR vs RD)

0.24 (0.11-0.54)<.001

HER2-enriched vs not

4.53 (2.11-9.73)<.001

TILs (continuous)

0.99 (0.97-1.00).14

Model 3:
EFS ≈ treatment + HR + stage + pCRB + subtype + signature
THL vs TH

0.31 (0.13-0.71).006

TL vs TH1.45 (0.70-3.02).32

HR (pos vs neg)

1.43 (0.71-2.90).32

Stage (III vs II)

2.01 (1.06-3.83).03

pCR (pCR vs RD)

0.30 (0.13-0.66).003

HER2-enriched vs not

4.28 (2.02-9.08)<.001

Signature (continuous)

0.63 (0.45-0.87).006

Model 4:
EFS ≈ treatment + HR + stage + pCRB + subtype + signature + TILs
THL vs TH

0.31 (0.13-0.72).006

TL vs TH1.45 (0.69-3.02).32

HR (pos vs neg)

1.43 (0.70-2.92).32

Stage (III vs II)

2.01 (1.05-3.84).03a391.96pCR (pCR vs RD)

0.30 (0.13-0.66).003

HER2-enriched vs not

4.28 (2.01-9.12)<.001

TILs (continuous)

1.00 (0.98-1.02).99

Signature (continuous)

0.63 (0.42-0.93).02

biomarkers, to stratify patients with early-stage ERBB2/HER2positive breast cancer into different prognostic groups. This
study supports that measurement of immune activation, either
by TIL measurement or by immune-related gene expression
profiling, is predictive of treatment response and that immunerelated gene expression is prognostic. In the presence of bothimmune biomarker types, iGESs, especially B-cell–related

ARTICLE INFORMATION
Accepted for Publication: September 21, 2022.
Published Online: January 5, 2023.
doi:10.1001/jamaoncol.2022.6288
Open Access: This is an open access article
distributed under the terms of the CC-BY License.
© 2023 Fernandez-Martinez A et al. JAMA
Oncology.
Author Affiliations: Lineberger Comprehensive
Cancer Center, University of North Carolina, Chapel

E8Model 4 vs model 2
LRT: P = .02

Downloaded From: https://jamanetwork.com/ on 02/05/2023

Model 4 vs model 3
LRT: P = .99

This was a comparative analysis ofnested multivariable Cox regression
models using an LRT. The signature
identification from the IgG signatureis IGG.Cluster Fan
BMCMedGenomics.2011
PMID.21214954.

signatures, outperform TILs for both pCR and prognosis, andthe combination of both biomarkers does not yield improved
prognostic value. These results highlight the essential role ofB cells in antitumor immunity and suggest that B-cell immunerelated gene expression provides valuable prognostic information for treatment escalation and de-escalation in patientswith early-stage ERBB2/HER2-positive breast cancer.

Hill (Fernandez-Martinez, Pascual, Hoadley, Spears,
Perou, Carey); Department of Genetics, Universityof North Carolina, Chapel Hill (Fernandez-Martinez,
Hoadley, Perou); Department of Medical Oncology,
Hospital Clínic de Barcelona, Barcelona, Spain(Pascual, Chic); Translational Genomics andTargeted Therapeutics in Solid Tumors, August Pi i
Sunyer Biomedical Research Institute (IDIBAPS),
Barcelona, Spain (Pascual, Brasó-Maristany, Chic,
Prat); SOLTI Breast Cancer Cooperative Group,
Barcelona, Spain (Pascual, Chic, Prat); Departmentof Pathology, White Plains Hospital, White Plains,

JAMA Oncology Published online January 5, 2023 (Reprinted)

Abbreviations: AIC, Akaike
information criterion; EFS, event-freesurvival; HR, hormone receptor;
LRT, likelihood ratio test; neg,
negative; pCR, pathologic complete
response; pCRB, in-breast pCR;
pos, positive; RD, residual disease;
TH, weekly paclitaxel plus
trastuzumab; THL, TH plus lapatinib;
TIL, tumor infiltrating lymphocyte;
TL, weekly paclitaxel plus lapatinib.

New York (Singh); Molecular Oncology Laboratory,
Vall d’Hebron Institute of Oncology, Barcelona,
Spain (Nuciforo); Department of Biostatistics,
University of North Carolina, Chapel Hill (Rashid);
Alliance Statistics and Data Management Center,
Mayo Clinic, Rochester, Minnesota (Ballman,
Campbell); Reveal Genomics, Barcelona, Spain(Pare, Prat); Yale Cancer Center, New Haven,
Connecticut (Krop); Department of Breast
Oncology, Dana-Farber Cancer Institute, Boston,
Massachusetts (Partridge); International Breast
Cancer Center, Barcelona, Spain (Cortés); Medical
jamaoncology.com

Immune-Related Gene Expression Signatures vs Tumor-Infiltrating Lymphocytes in ERBB2/HER2-Positive Breast Cancer

Oncology Department, Hospital Arnau de Vilanova,
Valencia, Spain (Llombart-Cussac); Department ofMedicine, University of Barcelona, Barcelona, Spain(Prat); Breast Cancer Unit, IOB-QuirónSalud,
Barcelona, Spain (Prat); Division of Medical
Oncology, Department of Medicine, School of
Medicine, University of North Carolina at Chapel
Hill, Chapel Hill (Carey).
Author Contributions: Dr Fernandez-Martinez had
full access to all of the data in the study and takes
responsibility for the integrity of the data and theaccuracy of the data analysis. Dr Carey served as
senior author.
Concept and design: Fernandez-Martinez, Ballman,
Spears, Perou, Carey.
Acquisition, analysis, or interpretation of data:
Fernandez-Martinez, Pascual, Singh, Nuciforo,
Rashid, Ballman, Campbell, Hoadley, Pare,
Brasó-Maristany, Chic, Krop, Partridge, Cortés,
Llombart-Cussac, Prat, Perou, Carey.
Drafting of the manuscript: Fernandez-Martinez,
Campbell, Pare, Prat, Perou, Carey.
Critical revision of the manuscript for important
intellectual content: Fernandez-Martinez, Pascual,Singh, Nuciforo, Rashid, Ballman, Hoadley, Spears,
Brasó-Maristany, Chic, Krop, Partridge, Cortés,
Llombart-Cussac, Prat, Perou.
Statistical analysis: Fernandez-Martinez, Pascual,Ballman, Campbell, Pare.
Obtained funding: Perou, Carey.
Administrative, technical, or material support:
Pascual, Nuciforo, Hoadley, Spears, Krop, Perou,
Carey.
Supervision: Rashid, Cortés, Llombart-Cussac, Prat,
Perou, Carey.
Conflict of Interest Disclosures:
Dr Fernandez-Martinez reported receiving grants
from National Cancer Institute during the conductof the study. Dr Pascual reported receiving
consulting fees from Novartis, speakers’ bureau
fees from AstraZeneca outside the submitted work,and personal fees from Pfizer. Dr Nuciforo reported
receiving grants from GSK to the institution to act
as the central academic laboratory during the
conduct of the study; and personal fees from
Novartis, Bayer, MSD Oncology, and Targos
Molecular Pathology outside the submitted work.
Dr Ballman reported receiving grants from National
Cancer Institute’s National Clinical Trials Network
for the Alliance during the conduct of the study andexpert witness fees from Johnson & Johnson,
Sanofi, Mylan Pharmaceuticals, and Janssen
outside the submitted work. Dr Pare reported
receiving a patent for an in vitro method for the
prognosis of patients with ERBB2/HER2-positive
breast cancer. Dr Brasó-Maristany reported
receiving a pending patent for an HER2DX assay(EP21383165). Dr Krop reported receiving personal
fees from Genentech-Roche; grants from
Genentech-Roche, Pfizer, and MacroGenics to his
institution; and personal fees from Bristol Meyers
Squibb, Daiichi-Sankyo, AstraZeneca, Novartis,
Merck, and Seagen outside the submitted work.
Dr Cortés reported receiving personal fees andgrants from Roche to his institution during the
conduct of the study; consulting and honoraria feesfrom Roche, Celgene, Daiichi Sankyo, Lilly, andMerck Sharp & Dohme; consulting fees fromCellestia, AstraZeneca, Seattle Genetics, Erytech,
Athenex, Polyphor, GSK, Leuko, Bioasis, Clovis
Oncology, Boehringer Ingelheim, Ellipses, HiberCell,
BioInvent, GEMoaB, Gilead, Menarini, Reveal
Genomics, and Zymeworks; grants from Ariad

jamaoncology.com

Downloaded From: https://jamanetwork.com/ on 02/05/2023

Pharmaceuticals, AstraZeneca, Baxalta
GmbH-Servier Affaires, Bayer Healthcare, Eisai, F.
Hoffman-La Roche, Guardanth Health, Merck Sharp& Dohme, Pfizer, PIQUR Therapeutics, Puma C, andQueen Mary University of London to his institution;accommodation and expenses fees from Roche,
Novartis, Eisai, Pfizer, Daiichi Sankyo, AstraZeneca,
and Gilead; honoraria from Novartis and Samsung
Bioepis outside the submitted work; and receiving apatent for pharmaceutical combinations of a Pi3k
inhibitor and a microtubule destabilizing agent (WO
2014/199294 A) and a patent for ERBB2/HER2 as apredictor of response to dual ERBB2/HER2
blockade in the absence of cytotoxic therapy (US2019/0338368 A1). Dr Llombart-Cussac reported
being a stockholder of Medsir; receiving grants
from Genentech-Roche, Daiichi-Sanyo, Pfizer, andNovartis outside the submitted work; and receiving
personal fees from Seagen. Dr Prat reported
receiving personal fees from Reveal Genomics
during the conduct of the study; personal fees from
Oncolytics Biotech, Roche, Novartis, AstraZeneca,
and Daiichi-Sankyo outside the submitted work;and receiving a patent for HER2DX licensed to
Reveal Genomics. Dr Perou reported receiving
grants from National Cancer Institute Breast SPORE
program P50-CA58223 during the conduct of thestudy; being an equity stockholder in and receiving
royalties from Bioclassifier LLC outside thesubmitted work; receiving a patent (US patent No.
12,995,459) with royalties paid from Bioclassifier;and being an equity owner in and consultant for
Reveal Genomics. No other disclosures werereported.
Funding/Support: Research reported in thispublication was supported by the National Cancer
Institute of the National Institutes of Health underawards U10CA180821, U10CA180882, andU24CA196171 (to the Alliance for Clinical Trials in
Oncology); R01CA229409 (Dr Carey);
UG1CA233160, UG1CA233180, UG1CA233290,
UG1CA233329, UG1CA233333, UG1CA233373, andP50CA058223 (Drs Carey and Perou); andU10CA180888 and UG1CA233160 (SWOG). The
research was also funded by The Breast Cancer
Research Foundation (Alliance; Drs Carey andPerou), Susan G. Komen (Drs Carey and Perou),
Fundación SEOM, Becas FSEOM para Formación en
Investigación en Centros de Referencia en el
Extranjero 2018 (Dr Pascual), and Fundación
Científica Asociación Española Contra el Cáncer:
Ayudas Investigador AECC 2021-INVES21943BRAS(Dr Brasó-Maristany).
Role of the Funder/Sponsor: The National Cancer
Institute of the National Institutes of Health, Susan
G. Komen, and The Breast Cancer Research
Foundation participated in the data design andconduct of the study; collection, management,
analysis, and interpretation of the data; andpreparation, review, and approval of themanuscript. Fundación SEOM and Fundación
Científica Asociación Española Contra el Cáncer
participated in the review and approval of themanuscript.
Disclaimer: The content is solely the authors’
responsibility and does not necessarily represent
the official views of the National Institutes of
Health.

Original Investigation Research

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