Abstract

   To find metastatic recurrence biomarkers of triple-negative breast
   cancer (TNBC) treated by neoadjuvant chemotherapy and anti-EGFR
   antibodies (NAT), we evaluated tumor genomic, transcriptomic, and
   immune features, using MSK-IMPACT assay, gene arrays, Nanostring
   technology, and TIL assessment on H&E. Six patients experienced a rapid
   fatal recurrence (RR) and other 6 had later non-fatal recurrences (LR).
   Before NAT, RR had low expression of 6 MHC class I and 13 MHC class II
   genes but were enriched in upregulated genes involved in the cell
   cycle-related pathways. Their TIL number before NAT in RR was very low
   (<5%) and did not increase after treatment. In post-NAT residual
   tumors, RR cases showed high expression of SOX2 and CXCR4. Our results
   indicate that high expression of cell cycle genes, combined with cold
   immunological phenotype, may predict strong TNBC resistance to NAT and
   rapid progression after it. This biomarker combination is worth
   validation in larger studies.

   Subject terms: Cancer genomics, Prognostic markers, Transcriptomics,
   Tumour immunology, Cancer

Introduction

   Triple-negative breast cancer (TNBC) is characterized by the lack of
   expression of estrogen receptor (ER) and progesterone receptor (PR) and
   the absence of amplification of ERBB2. It encompasses a large spectrum
   of breast malignancies and is highly heterogeneous in terms of
   histology, molecular features, and clinical behavior^[50]1. Although
   some of these tumors are characterized by low histological grade and
   good long-term prognosis after local treatment, the majority present
   with a high grade and aggressive nature reflected by an early peak of
   recurrence, fatal distant metastases, and the 5-year overall survival
   (OS) rate lower than the other subtypes of breast cancer^[51]2,[52]3.
   Therefore, finding reliable predictors of life-threatening recurrences
   is an unmet need for TNBC.

   Most TNBC recurrences develop within 5 years after the diagnosis^[53]4,
   and it is accepted that the risk of recurrence is higher in TNBC
   patients with a big residual tumor (RT) after neoadjuvant treatment
   (NAT)^[54]5. Balko and collaborators have elegantly shown that post-NAT
   TNBC RTs carry numerous genomic alterations and that 90% of these
   tumors have at least one actionable molecular lesion^[55]6. This study
   posited that molecular alterations found in the post-NAT residual
   disease may be the same present in micrometastases that will cause
   disease recurrence.

   Beyond genomic anomalies, recent studies have highlighted the
   importance of TNBC immunological subtype both for response to NAT and
   for further clinical evolution. TNBCs rich in tumor-infiltrating
   lymphocytes (TILs), either before or after NAT, have better outcomes
   than those poor in TILs^[56]7,[57]8. Besides TIL quantity, gene
   expression profiling has revealed the existence of several
   prognostically relevant TNBC subtypes, with different immune
   microenvironment composition and immune response within the breast
   tumor tissue^[58]9,[59]10.

   However, none of those subtypes or other gene signatures that emerged
   as recurrence predictors in TNBC is sufficiently validated to enter the
   clinics^[60]11–[61]17. In addition, no biological marker has been
   proposed to identify TNBCs that will progress shortly after NAT. Tumor
   relapses occurring within the first year after NAT are considered
   manifestations of primary chemoresistance and their long-term favorable
   outcome is exceedingly rare. In those tumors, chemotherapy can even
   stimulate metastatic progression by selecting the cells with high
   stemness and proliferation or by modifying the tumor microenvironment
   to favorize the survival and multiplication of metastatic tumor
   cells^[62]18–[63]21. Therefore, the patients carrying such tumors would
   benefit from early identification, ideally before NAT, and be offered
   more advanced therapy approaches than the standard anthracycline-taxane
   regimens.

   On the other hand, real-life observations in the clinic confirm that
   certain TNBC patients will never experience recurrence, even left with
   a big RT and no adjuvant chemotherapy after NAT. If readily identified
   at the post-NAT surgery, this patient population could be included in
   clinical trials testing de-escalation of adjuvant treatments. However,
   studies showing how to recognize either the TNBCs which will rapidly
   progress after NAT or the ones which will remain under long-term
   control despite big post-NAT residual tumors, are lacking.

   Here we report a comprehensive genomic and transcriptomic analysis,
   integrated with lymphocyte infiltration, of pre-treatment and post-NAT
   samples from TNBC patients undergoing chemotherapy and EGFR
   blockade^[64]22,[65]23.

Results

Patient population

   We analyzed a total of 62 patients, 12 of them experiencing metastatic
   recurrences. Six patients recurred within a year after surgery (group
   A) and rapidly deceased from metastatic disease. Six patients had later
   recurrences, mostly without a fatal outcome (in 1–5 years after
   surgery, group B) (Table [66]1 and Supplementary Fig. [67]1). Since we
   were interested in comparing molecular characteristics of the recurring
   tumors versus those of non-recurring tumors of various sizes, we
   further classified the non-recurring tumors into 4 groups: group C (8
   patients, breast RT size, defined by its greatest dimension, >30 mm);
   group D (4 patients, RT size ≤30 mm but >20 mm); group E (18 patients,
   RT size ≤20 mm but not pCR); group F (20 patients, pCR). The only two
   patients with pCR and a recurrence were integrated into group B
   (patients 38 and 72, Table [68]1 and Supplementary Fig. [69]1). This
   arbitrary classification was used as we could not determine residual
   cancer burden in all cases because of a lack of complete breast and
   lymph node material in some cases treated outside of Centre Jean Perrin
   (external participants in the trials).

Table 1.

   Patient clinical characteristics.
   Pt ID Group Trial Age TN Stage Histological type Response to Th Breast
   RT size (mm) Number of involved LNs post-NAT Recurrence site TTR, from
   surgery (months) Survival from surgery until death or the end of
   follow-up (months)
   65 A PTMB 55 T2N0 IIA NST Non-pCR 30 0 Lungs, bone 6 8
   49 A PTMB 55 T3N1 IIIA NST Non-pCR 70 9 Liver 3 8
   9 A PTMB 44 T2N0 IIA NST Non-pCR 50 1 Brain 5 6
   52 A PTMB 67 T2N0 IIA NST Non-pCR 80 6 Breast, lungs 5 7
   46 A CTX 46 T2N0 IIA NST Non-pCR 45 0 Lungs 12 17
   71 A PTMB 42 T2N0 IIA NST Non-pCR 18 2 Lungs, liver 9 15
   38 B PTMB 50 T3N0 IIB NST pCR 0 0 Solitary brain metastasis 40 60
   10 B PTMB 73 T3N2 IIIA Apocrine Non-pCR 60 9 Lungs 34 60
   33 B CTX 64 T3N0 IIB NST Non-pCR 20 0 Lungs 46 60
   44 B CTX 50 T2N1 IIB NST Non-pCR 55 Lungs 17 60
   72 B PTMB 65 T2N0 IIA NST pCR 0 0 Brain 23 24
   73 B PTMB 57 T2N0 IIA NST Non-pCR 5 1 Lungs, liver 17 26
   32 C PTMB 35 T3N1 IIIA NST Non-pCR 45 2 None NA 60
   60 C CTX 67 T3N1 IIIA NST Non-pCR 60 0 None NA 60
   54 C PTMB 29 T2N0 IIA NST Non-pCR 55 1 None NA 60
   23 C CTX 54 T3N1 IIIA NST Non-pCR 50 0 None NA 60
   50 C PTMB 42 T2N0 IIA NST Non-pCR 45 0 None NA 60
   21 C CTX 61 T2N1 IIB NST Non-pCR 35 0 None NA 60
   11 C PTMB 42 T2N1 IIB NST Non-pCR 35 2 None NA 60
   48 C PTMB 53 T3N2 IIIA NST Non-pCR 35 0 None NA 60
   68 D PTMB 62 T2N1 IIB NST Non-pCR 30 1 None NA 60
   34 D PTMB 44 T2N0 IIA NST Non-pCR 25 0 None NA 60
   45 D CTX 42 T2N0 IIA NST Non-pCR 25 0 None NA 60
   56 D CTX 54 T2N0 IIA NST Non-pCR 25 0 None NA 60
   13 E PTMB 50 T2N0 IIA NST Non-pCR 20 0 None NA 60
   69 E PTMB 40 T3N0 IIB NST Non-pCR 20 0 None NA 60
   67 E PTMB 54 T2N0 IIA NST Non-pCR 20 1 Breast NA 60
   64 E CTX 37 T2N1 IIB NST Non-pCR 20 1 None NA 60
   40 E CTX 59 T2N0 IIA NST Non-pCR 20 0 None NA 60
   57 E CTX 53 T3N0 IIB NST Non-pCR 16 0 None NA 60
   70 E CTX 28 T2N1 IIB NST Non-pCR 15 4 None NA 60
   30 E CTX 47 T3N0 IIB NST Non-pCR 12 4 None NA 60
   28 E CTX 34 T2N1 IIB NST Non-pCR 10 0 None NA 60
   36 E PTMB 39 T3N0 IIB NST Non-pCR 9 0 None NA 60
   35 E CTX 38 T2N2 IIIA NST Non-pCR 7 3 None NA 60
   19 E CTX 52 T3N0 IIB NST Non-pCR 2 3 None NA 60
   74 E PTMB 35 T2N0 IIA NST Non-pCR 6 0 None NA 60
   75 E CTX 60 T2N0 IIA NST Non-pCR 3 0 None NA 60
   76 E PTMB 48 T2N1 IIB NST Non-pCR 2 2 None NA 60
   84 E PTMB 43 T3N1 IIIA NST Non-pCR 7 2 None NA 60
   85 E CTX 63 T2N1 IIB NST Non-pCR 12 0 None NA 60
   86 E CTX 46 T2N1 IIB NST Non-pCR 4 0 None NA 60
   26 F PTMB 27 T2N0 IIA NST pCR 0 0 None NA 60
   47 F PTMB 52 T2N1 IIB NST pCR 0 0 None NA 60
   16 F CTX 64 T2N0 IIA NST pCR 0 0 None NA 60
   15 F PTMB 43 T2N0 IIA NST pCR 0 0 None NA 60
   22 F CTX 54 T2N0 IIA NST pCR 0 0 None NA 60
   27 F PTMB 40 T2N0 IIA NST pCR 0 0 None NA 60
   39 F CTX 48 T2N0 IIA NST pCR 0 0 None NA 60
   41 F PTMB 30 T2N0 IIA NST pCR 0 0 None NA 60
   3 F PTMB 41 T2N0 IIA NST pCR 0 0 None NA 60
   4 F PTMB 31 T2N1 IIB NST pCR 0 0 None NA 60
   6 F PTMB 65 T2N0 IIA NST pCR 0 0 None NA 60
   8 F PTMB 36 T2N1 IIB NST pCR 0 0 None NA 60
   43 F CTX 47 T2N0 IIA NST pCR 0 0 None NA 60
   77 F PTMB 49 T2N1 IIB NST pCR 0 0 None NA 60
   78 F PTMB 56 T3N0 IIB NST pCR 0 0 None NA 60
   79 F CTX 61 T2N0 IIA NST pCR 0 0 None NA 60
   80 F CTX 33 T2N1 IIB NST pCR 0 0 None NA 60
   81 F PTMB 50 T2N0 IIA NST pCR 0 0 None NA 60
   82 F PTMB 65 T2N1 IIA NST pCR 0 0 None NA 60
   83 F PTMB 42 T2N0 IIA NST pCR 0 0 None NA 60
   [70]Open in a new tab

   Legend: RT residual tumor, PTMB panitumumab trial: panitumumab
   (Vectibix^®), 9 mg/kg intravenous cycles combined with FEC × 4
   (500/100/500 mg/m^2), followed by docetaxel × 4 (100 mg/m^2); CTX,
   cetuximab trial: 18 intravenous weekly infusions (Day1/8/15) of
   cetuximab (Erbitux^®) (first infusion: 400 mg/m^2; subsequently:
   250 mg/m^2) combined with docetaxel (100 mg/m^2) on Day1 q3 weekly for
   six cycles; NST non-special type, LNs lymph nodes, NAT neoadjuvant
   treatment, TTR time to recurrence, pre, a sample taken before
   neoadjuvant treatment; post, a sample is taken after neoadjuvant
   treatment; pCR pathological complete response, NA not applicable.

   Interpretable targeted panel sequencing (MSK-IMPACT) data were obtained
   for 45 patients, 26 from the PTMB trial and 19 from the CTX trial. For
   all patients who reached pCR (n = 13), only the pre-therapy sample was
   assessed, whereas pre-NAT and post-NAT matched samples were available
   in 17 of the 32 non-pCR patients. For 15 non-pCR patients, only the
   post-NAT sample was analyzed (Table [71]2).

Table 2.

   Samples used for molecular analyses and results of TIL amount
   assessment.
   Pt ID Group Trial Sample for IMPACT analysis Samples for gene array
   (GA) and NanoString (NS) analysis TILs (change of % from before to
   after treatment)
   65 A PTMB Post NS 10 → <1
   49 A PTMB Post GA + NS 1 → 5
   9 A PTMB Pre + post GA + NS 1 → 1
   52 A PTMB Post - 40 → 5
   46 A CTX Pre + post NS  < 1 → 5
   71 A PTMB - GA 10 → 10
   38 B PTMB Pre - 80 → NA (pCR)
   10 B PTMB Pre + post NS  < 1 → 5
   33 B CTX Pre + post NS <1 → <1
   44 B CTX Pre + post NS <1 → <1
   72 B PTMB - GA 100 → NA (pCR)
   73 B PTMB - GA 1 → NA (very small RT)
   32 C PTMB Pre + post GA + NS 10 → 5
   60 C CTX Post NS <1 → <1
   54 C PTMB Pre + post GA 80 → 30
   23 C CTX Pre + post NS 70 → 30
   50 C PTMB Pre + post GA + NS 5 → 1
   21 C CTX Post GA + NS 2 → 40
   11 C PTMB Pre + post GA + NS 5 → 5
   48 C PTMB Post GA + NS <1 → 5
   68 D PTMB Post GA 2 → 20
   34 D PTMB Pre + post GA + NS 40 → 20
   45 D CTX Post GA + NS 10 → 40
   56 D CTX Post - <1 → 5
   13 E PTMB Pre + post GA 80 → 40
   69 E PTMB Post - 20 → 70
   67 E PTMB Post NS 30 → 20
   64 E CTX Post GA + NS 2 → 30
   40 E CTX Pre + post GA + NS 2 → 20
   57 E CTX Post GA + NS 1 → 1
   70 E CTX Post NS <1 → 20
   30 E CTX Pre + post GA + NS 10 → 10
   28 E CTX Pre + post GA + NS 70 → 5
   36 E PTMB Post - 5 → 20
   35 E CTX Pre + post NS 100 → 100
   19 E CTX Pre + post - 40 → 40
   74 E PTMB - GA 50 → 50
   75 E CTX - GA + NS 10 → 20
   76 E PTMB - GA 90 → 90
   84 E PTMB - NS 2 → 90
   85 E CTX - NS 40 → 70
   86 E CTX - NS 30 → 90
   26 F PTMB Pre GA 20 → NA
   47 F PTMB Pre GA 2 → NA
   16 F CTX Pre GA 2 → NA
   15 F PTMB Pre 100 → NA
   22 F CTX Pre GA 60 → NA
   27 F PTMB Pre GA 2 → NA
   39 F CTX Pre GA 90 → NA
   41 F PTMB Pre GA 40 → NA
   3 F PTMB Pre GA 40 → NA
   4 F PTMB Pre GA 30 → NA
   6 F PTMB Pre GA 80 → NA
   8 F PTMB Pre - 80 → NA
   43 F CTX - GA 100 → NA
   77 F PTMB - GA 40 → NA
   78 F PTMB - GA 5 → NA
   79 F CTX - GA 20 → NA
   80 F CTX - GA 40 → NA
   81 F PTMB - GA 90 → NA
   82 F PTMB - GA 100 → NA
   83 F PTMB - GA 30 → NA
   [72]Open in a new tab

   RT residual tumor, PTMB panitumumab trial, CTX cetuximab trial, pre,
   sample taken before neoadjuvant treatment; post, sample taken after
   neoadjuvant treatment; pCR pathological complete response; NA not
   applicable. Samples for gene array and NanoString are pre- and
   post-therapy, respectively.

   Gene expression analysis by gene arrays was performed in 41 pre-NAT
   samples, whereas NanoString technology was used to analyze 28 post-NAT
   samples (Table [73]2 and Supplementary Fig. [74]1).

Tumor genomic features

   As expected, the vast majority of the tumors carried aberrations in
   TP53 (44/45) (Fig. [75]1), which is consistent with the TNBC described
   genotype^[76]24. Interestingly, no mutations were found in one post-NAT
   sample from group A (patient 49, Table [77]1). In general, we observed
   copy number heterogeneity among the groups, with group F being an
   exception with very low copy number alterations (CNA) (Fig. [78]2),
   suggesting that genomic instability does not play a role in determining
   the aggressiveness of these tumors.

Fig. 1. A heatmap depicting somatic mutations in 61 pre/post-treatment TNBC
samples.

   [79]Fig. 1
   [80]Open in a new tab

   Samples are shown as columns and are ordered by pre/post status and by
   the group. Genes that were altered in ≥2 samples are shown with colored
   squares corresponding to the colors in the legend. A rapidly recurring
   tumors (<1 year post-surgery); (B) tumors with standard recurrences
   (1–5 years after surgery); (C, D, and E) tumors without pathological
   complete response (pCR) to neoadjuvant treatment (NAT) and without
   recurrences; (F) tumors with pCR to NAT and without recurrences.
   Samples are presented by MSK-IMPACT assay identifiers (X-axis of the
   heatmap, identifiers of type tnbc-123-A or tnbc-123-AB).

Fig. 2. Copy number alterations in 61 pre/post-treatment TNBCs.

   [81]Fig. 2
   [82]Open in a new tab

   A heatmap shows the amplifications, gains, losses, and homozygous
   deletions across all samples.

   We observed a slight increase in mutations in the post-treatment
   samples, but this was not statistically significant (Fig. [83]1).
   Interestingly, among the 8 most mutated genes after TP53, 5 encode for
   epigenetic regulators: KMT2D, KDM6A, SETD2, NSD1, and MGA.

Tumor gene expression profiles

   In the study of Masuda et al.^[84]25, TNBC subtypes are associated with
   patient outcomes. In our study, we classified patients into 6 subtypes
   suggested by Lehmann et al.^[85]26, based on gene array data
   (pre-treatment samples), to examine the enrichment of TNBC subtypes in
   our patient groups. However, we did not observe an enrichment of
   specific TNBC subtypes in any of our patient groups (Supplementary Fig.
   [86]2), suggesting that TNBC subtypes cannot be used to predict patient
   outcomes in this cohort. Further, there was no statistically
   significant difference in baseline gene expression between the tumors
   which recurred (groups A–B) and those which did not (groups C-F).
   However, differential gene expression analysis, comparing group A
   tumors with group F tumors (Supplementary Fig. [87]3), identified 12
   HLA genes, which were significantly downregulated in group A. Moreover,
   all 6 MHC I class and 13 MHC II class HLA genes had lower expression in
   group A tumors (Fig. [88]3a), indicating that these tumors would elicit
   a low immune response. It should be noted that one of group A patients,
   patient 71 (Fig. [89]3a, Table [90]1, respectively), shows high
   expression in the MHC I class, presenting heterogeneity of HLA
   expression.

Fig. 3. Molecular characteristics of the intratumor immune response.

   [91]Fig. 3
   [92]Open in a new tab

   a Heatmap of gene expression in the HLA family. Differentially
   expressed genes identified from A vs. F are marked in bold. Z-score of
   expression level was calculated for each gene across all 41 patients.
   Samples are presented with their original institutional identifiers.
   A–F, recurrence groups; BL1, basal-like 1; BL2, basal-like 2, IM,
   immunomodulatory; LAR, luminal androgen receptor; M, mesenchymal; MSL,
   mesenchymal stem-like; UNS, unclassified. Samples are presented by
   MSK-IMPACT assay identifiers (X-axis of the heatmap, identifiers of
   type tnbc-123-A or tnbc-123-AB). b Immune cytolytic activity (CYT).
   p-value between group A tumors and others was calculated using a
   two-tailed Student’s t-test.

   To confirm the low intratumor immune activity of group A tumors, we
   calculated the CYT score, a hallmark of cytolytic activity of the tumor
   immune infiltrate, as proposed by Rooney et al.^[93]27. As shown in
   Fig. [94]3b, group A tumors show significantly lower CYT scores
   (P-value <0.03, 95% CI: −3.72, −0.47) in comparison with the tumors
   with better prognosis, indicating that the intratumor immune activity
   in the group A tumors is indeed decreased, which might be one of the
   features permitting their rapid progression after therapy.

   As IFN-γ is a key cytokine produced by the immune cells during the
   anti-tumor immune response, we examined the expression of the
   IFN-γ-related genes^[95]28 and found that, except for patient FAL-SA
   (patient 71, Table [96]1), another two patients in group A had
   extremely low baseline expression of many IFN-γ-related and other
   immune genes (Fig. [97]4). Together with low HLA gene expression and
   CYT scores, these findings reveal a “cold” immunological profile of the
   rapidly recurring tumors, which existed already before the NAT.

Fig. 4. Expression of the IFN-γ-related and other immune genes.

   [98]Fig. 4
   [99]Open in a new tab

   Z-score of expression level was calculated for each gene across all 41
   patients. Samples are presented with their original institutional
   identifiers. A–F, recurrence groups; BL1 basal-like 1, BL2 basal-like
   2, IM immunomodulatory, LAR luminal androgen receptor; M mesenchymal,
   MSL mesenchymal stem-like; UNS unclassified.

   Through gene ontology (GO) enrichment analysis in “Molecular Function”
   terms, we observed 7 out of the top 10 enriched GO terms are related to
   downregulated cytokine and chemokine activities, including cytokine
   receptor activity (GO:0004896), cytokine binding (GO:0019955), cytokine
   activity (GO:0005125), cytokine receptor binding (GO:0005126),
   chemokine receptor binding (GO:0042379), and chemokine activity
   (GO:0008009) (Supplementary Fig. [100]4a). MHC protein complex binding
   (GO:0023023) is also enriched in downregulated genes, consistent with
   low HLA gene expression in group A tumors described above. Reactome
   pathway enrichment analysis identified enriched pathways in
   downregulated genes, for example, “chemokine receptors bind
   chemokines”, “interferon-gamma signaling”, “signaling by interleukins”,
   and so on (Supplementary Fig. [101]4b). Their pathway networks indicate
   that these pathways are immune-related (Supplementary Fig. [102]4c).

   Another important difference in the baseline gene expression between
   group A and group F tumors was significantly higher expression of the
   cell cycle-related genes in group A. By gene set enrichment analysis we
   saw that the pathways enriched in those upregulated genes were
   “Polo-like kinase-mediated events”, “cyclin A/B1/B2 associated events
   during G2/M transition”, and “Chk1/Chk2(Cds1) mediated inactivation of
   Cyclin B: Cdk1 complex” (Supplementary Fig. [103]5). We further
   performed reactome gene set enrichment analysis using expression fold
   change values calculated from differential expression analysis between
   A and F and observed that cell cycle-related pathways are enriched in
   upregulated genes, including genes involved in different stages of the
   cell cycle (Fig. [104]5, left), for example, “S phase” and “separation
   of sister chromatids”, and genes involved in the regulation of cell
   cycle (Fig. [105]5, right), for example, “cell cycle checkpoints” and
   “mitotic spindle checkpoint”.

Fig. 5. Representative gene set enrichment analysis plot showing upregulation
of cell cycle-related genes.

   [106]Fig. 5
   [107]Open in a new tab

   On x-axis, genes are ranked based on log[2] fold change of gene
   expression level. From top to bottom, they are enrichment score of
   pathways on each gene, log[2] fold change of expression level.

   To examine genes related to metastasis, we compared gene expression
   data produced by the NanoString gene panel assessing genes involved in
   cancer progression, in post-NAT residues of tumors with and without
   recurrences. As shown in Supplementary Fig. [108]6, we observed that
   SOX2, a typical embryonic/stemness gene, and CXCR4, a well-known
   metastasis-related gene, are upregulated in group A tumors; AREG,
   encoding for amphiregulin, was upregulated in group B tumors. These
   data suggest that SOX2, CXCR4, and AREG may have a role in TNBC
   progression after NAT.

Tumor-infiltrating lymphocytes (TILs)

   In the RT of patients from group A, TILs were strikingly low before NAT
   ( ≤5% in all but one patient), which was concordant with gene
   expression showing low intratumor immune activity. Moreover, no
   patients in group A had increased TILs after NAT (Table [109]2). In
   group B, the baseline TIL density was also low (<1%) in 4 out of 6
   evaluated cases. As in group A, no increase in TILs was observed after
   NAT, but 2 patients from this group reached pCR. Interestingly, one of
   the pCR cases had 100% TILs before NAT; however, she was diagnosed with
   a brain metastasis 23 months post-NAT and died a month after (patient
   72, Table [110]2).

   Most pCR cases had moderate or high amounts of TILs: 18 out of 22 cases
   had ≥20% of TILs before NAT, and 10 cases corresponded to the
   lymphocyte-predominant breast cancer (≥50% TILs) (Table [111]2). Two
   out of 23 pCR cases recurred; interestingly, both were very rich in
   TILs (80–100%) and both developed a brain metastasis, one of which was
   fatal.

   These data indicate that very low TIL density (<5%), before NAT or in
   the post-NAT RT, could be an indicator of metastatic progression,
   however if associated with gene expression profile showing low
   anti-tumor immune activity, as seen in group A.

Discussion

   In this work, we present results obtained by molecular analyses of TNBC
   tissues sampled before and after NAT containing anti-EGFR antibodies
   and chemotherapy.

   In search of molecular features associated with patient outcome, we
   first observed a marked heterogeneity of the analyzed samples in terms
   of mutation and CNA profile, without any specific alteration associated
   with metastatic recurrences. This is in line with other reports where
   they detect potentially actionable mutations in TNBC tissue but are
   unable to predict disease recurrence^[112]6. These findings may suggest
   that mutational profiling of TNBC is not the method of choice for the
   discovery of recurrence biomarkers and that other molecular analyses
   should be preferentially used for that purpose.

   By analysis of gene expression, we distinguished a set of alterations
   characterizing TNBCs which progressed very rapidly after NAT
   completion. All but one of the six patients carrying those tumors died
   of metastatic disease in less than 6 months after surgery (data not
   shown). Rapid post-NAT progressors are relatively rare among TNBCs
   (3–5%, personal communication from Dr Mouret-Reynier, Centre Jean
   Perrin) and typically are undistinguishable from other non-responders
   in clinical trials. Our study showed that TNBCs with rapid metastatic
   progression after NAT containing anti-EGFR and chemotherapy had,
   already before therapy, two important biological characteristics: very
   low expression of several HLA class I or II genes and high expression
   of cell cycle-related genes.

   MHC/HLA class I loss or downregulation is one of the main mechanisms of
   cancer immune escape, resulting in decreased T-cell cytolytic
   anti-tumor response^[113]29. In our series, the rapidly recurring
   tumors had both low expression of MHC I and -II genes and low cytolytic
   activity scores, indicating the absence of the anti-tumor immune
   reaction. In breast cancer in general, loss of HLA-I is associated with
   poor prognosis^[114]30,[115]31. This was demonstrated also in TNBC,
   where higher expression of HLA class 2 molecules is linked to
   significantly longer disease-free survival^[116]16,[117]32. One group
   has recently developed a 36-gene assay based on Nanostring technology,
   the MHCII Immune Activation Assay, which identifies TNBC patients with
   low risk of recurrence; patients with these tumors can be treated by
   immunotherapy instead of classical cytotoxic therapy^[118]16.
   Similarly, high expression of a gene signature containing HLA-DRA,
   HLA-E, IDO1, CXCL9, CXCL10, STAT1, and GZMB was found to be associated
   with TNBCs without relapse in 5 years after NAT^[119]33.

   Besides low expression of the HLA genes, the rapidly recurring tumors
   in our series were characterized also by low expression of the IFN-γ
   signature and several other genes involved in the immune response
   within the tumor site. Most of those tumors had a very low amount of
   TILs before NAT and none of them increased TILs after NAT. This could
   explain their high resistance to the administered treatment, considered
   as capable of inducing immunogenic cell death and increasing tumor
   infiltration by cytolytic lymphocytes. Two recent studies proposed that
   the immune infiltration of post-NAT TNBC residues can influence the
   metastatic evolution of the disease. Patients with TNBC highly
   infiltrated by lymphocytes after NAT have longer metastasis-free
   survival than the ones having post-NAT residues with reduced amounts of
   TILs^[120]34. Moreover, low levels of post-NAT TILs in TNBC were shown
   to be associated with activation of the RAS/MAPK pathway, one of the
   proposed mechanisms of resistance to chemotherapy and metastatic
   progression of TNBC^[121]35. Thus, our data indicate that TNBC with
   very low TILs at the baseline and after NAT may represent a potentially
   aggressive category.

   We also showed that the rapidly recurring tumors had a baseline
   expression of genes involved in the regulation of the cell cycle much
   higher than the tumors which experienced pCR to NAT and never recurred,
   but also higher than any other group in our cohort (data not shown).
   High pre-NAT proliferation, measured by the Ki67 index, has mostly been
   reported as a predictor of good response (pCR) to NAT;^[122]36 however,
   very high Ki67 indices (≥50%) were associated shown to be associated
   with progressive disease^[123]37,[124]38. It has been postulated that
   activation of multiple receptor tyrosine kinases (RTKs) such as EGFR,
   IGF1R, or MET is responsible, at least in part, for the high
   proliferation of the progressive TNBCs^[125]26. In such cases, blockade
   of several RTKs might be more efficacious than single-type receptor
   blockade.

   Our findings indicate that simultaneous assessment of the expression of
   genes involved in tumor proliferation and those involved in the immune
   response might be an effective way to stratify TNBC patients. In a
   series of 1954 breast cancers of all molecular subtypes, high
   expression of a proliferation-related signature and of all three immune
   metagenes was associated with longer distant metastasis-free survival
   (DMFS). However, in those highly proliferative breast cancers, low
   expression of any of the immune metagenes was associated with short
   DMFS despite high expression of the other two^[126]39. These data
   support the high importance of strong immune control of the highly
   proliferating breast cancers for a good outcome. On the other hand, the
   association of high proliferation and immunological silence would favor
   rapid tumor progression, as observed in our patient cohort.

   Our analysis of the post-NAT residues, although very limited,
   highlighted two pro-metastatic genes, SOX2 and CXCR4, as potential
   recurrence predictors. SOX2 has been shown to promote proliferation and
   metastasis in TNBC^[127]40. Similarly, several previous studies have
   shown an association between high CXCR4 expression and breast cancer
   metastasis^[128]41. Interestingly, a recent study showed a strong
   correlation between high CXCR4 expression and high TIL count^[129]42,
   whereas in our study high CXCR4 expressors were devoid of TILs. More
   work is therefore necessary to fully explore the relationship between
   SOX2, CXCR4, and the immune microenvironment of TNBC, however these
   three parameters seem to have the capacity to predict patient outcomes
   in TNBC.

   Besides potential biomarkers of rapid recurrence after NAT, we did not
   reveal biomarkers that could stratify the entire cohort into prognostic
   groups. This likely can be explained by a small number of recurrences
   as well as of patients/samples analyzed and the heterogeneity of the
   cohort. These characteristics of the cohort we analyzed here are surely
   the major limitations of the study. However, since the trials we
   conducted were the first trials of neoadjuvant EGFR blockade in TNBC,
   we wanted to analyze the available tissue material, even limited, to
   generate at least some hypotheses. Another limitation of this study was
   our inability to calculate residual cancer burden^[130]43 because of
   the lack of the necessary material in some surgical specimens (due to
   the multicentricity of the clinical trials). It would have been
   interesting to see whether any of the evaluated molecular features had
   a specific relationship with any of the RCB categories.

   In conclusion, we found that TNBC with high baseline expression of cell
   cycle genes, associated with a “cold” immune microenvironment, may have
   a very poor prognosis following neoadjuvant treatment containing
   chemotherapy and anti-EGFR antibodies. Therefore, high pre-treatment
   proliferation and low intratumor immune response are worth evaluation
   in larger studies, on cohorts treated by various neoadjuvant
   treatments, to verify their general and treatment-specific prognostic
   value.

Methods

Patients and tumor samples

   The available material (formalin-fixed, paraffin-embedded (FFPE) tumor
   tissues or extracted DNA) from the patients enrolled in clinical trials
   [131]NCT00933517 (panitumumab trial, PTMB) and [132]NCT00600249
   (cetuximab trial, CTX) was used. Details on the inclusion criteria,
   treatment, response, and non-genomic predictive biomarkers are
   previously published^[133]22,[134]23. The clinical follow-up of 5 years
   after surgery ended in August 2017. Recurrence was defined as the
   appearance of any invasive cancer deposit. All local and most distant
   recurrences were histologically confirmed, except brain metastases
   which were diagnosed by imaging.

   The greatest dimension of post-NAT breast residual tumor and the number
   of involved axillary lymph nodes were retrieved from the pathology
   reports received from the participating centers in the trials. The
   tumors were all but one of Scarff-Bloom-Richardson (SBR) grade 3. The
   only tumor of SBR grade 2 was an apocrine carcinoma in group E. No SBR
   grade change was observed after the NAT, in comparison to the pre-NAT
   grade.

   Quantity of tumor-infiltrating lymphocytes (TILs) was estimated on
   H&E-stained FFPE pre-NAT and post-NAT tumor tissue sections, according
   to the recommendations of the International Immuno-Oncology Biomarker
   Working Group on Breast Cancer^[135]44,[136]45.

Targeted exome sequencing

   DNA samples extracted from formalin-fixed, paraffin-embedded TNBCs
   (n = 62) were subjected to MSK-IMPACT targeted sequencing at the MSKCC
   Integrated Genomics Operation as previously described^[137]46. Briefly,
   raw sequence reads were aligned to the reference human genome GRCh37
   using the BURROWS-WHEELER ALIGNER (BWA 0.7.15)^[138]47. Local
   realignment, duplicate read removal, and base quality score
   recalibration were performed using the GENOME ANALYSIS TOOLKIT (GATK
   3.7)^[139]48. Somatic single nucleotide variants (SNVs) were called
   using MUTECT (1.1.7)^[140]49, and small insertions and deletions
   (indels) were identified using STRELKA (1.0.15)^[141]50, VARSCAN2
   (2.3.7)^[142]51, LANCET (1.0.0)^[143]52, and SCALPEL (0.5.3)^[144]53
   and further curated by manual inspection. SNVs and indels outside of
   target regions were filtered out, as were SNVs and indels for which the
   variant allele fraction (VAF) in the tumor sample was <5 times that of
   the paired normal VAF as previously described^[145]46,[146]54. Finally,
   SNVs and indels found at >5% global minor allele frequency in dbSNP
   (build 137) and >5% global allele frequency in EXAC (0.3.1) were
   discarded. Somatic copy number alterations and loss of heterozygosity
   were obtained using FACETS^[147]55. The cancer cell fractions (CCF) of
   all mutations were computed using ABSOLUTE (1.0.6)^[148]56. A mutation
   was classified as clonal if its probability of being clonal was
   >50%^[149]57 or if the lower bound of the 95% confidence interval of
   its CCF was >90%^[150]46,[151]54. Mutations that did not meet the above
   criteria were considered subclonal. A combination of in silico
   functional predictors was used to define the potential functional
   impact of each missense SNV as previously described^[152]46,[153]58.
   Mutation hotspots were assigned according to Chang et al.^[154]59.

Gene expression by gene arrays

   Nucleic acids were extracted from frozen tumor tissue (pre-NAT samples)
   using AllPrep DNA/RNA mini kit (Qiagen France SAS, Courtaboeuf, France)
   according to the manufacturer’s protocol. RNA quality was verified
   using the 2100 BioAnalyzer (Agilent Technologies). The extracted
   material was sent to Helixio (Saint-Beauzire, France), where it was
   hybridized with gene arrays (Human SurePrint, Agilent Technologies
   France, Les Ullis, France). The raw data were transferred to Memorial
   Sloan Kettering Cancer Center for bioinformatics and statistical
   analysis.

   Limma package^[155]60 was applied to process Agilent microarray data
   and identify differentially expressed genes between progressive tumors
   and tumors with pathological complete response. Background correction
   was performed on expression intensities before normalization by the
   parameter of “expnorm”. The median value was taken when a gene has
   multiple probes. Then, “cyclicloess” method, which applies loess
   normalization to all possible pairs of arrays, was used to normalize
   corrected intensities. Fold change of expression intensities and
   P-values were estimated, which were used to identify differentially
   expressed genes.

   We performed GO^[156]61 and Reactome pathway^[157]62 enrichment
   analysis with clusterProfiler^[158]63 and ReactomePA^[159]64.
   Over-representation analysis and gene set enrichment analysis were
   performed. Benjamini-Hochberg method was used to calculate adjusted
   p-values.

   TNBCtype was used to classify samples into transcriptomic
   subtypes^[160]26,[161]65.

   Immune cytolytic activity of the local immune infiltrate was quantified
   by measuring expression of the granzyme A (GZMA) and perforin (PRF1)
   genes, as proposed by Rooney et al.^[162]27. We calculated the CYT
   score by averaging the expression level of GZMA and PRF1 for each
   microarray sample^[163]66.

Gene expression by NanoString

   RNA was isolated from FFPE samples of post-NAT residual tumors using
   the High Pure FFPET RNA Isolation Kit (Roche Diagnostics, Meylan,
   France), according to the manufacturer’s instructions. RNA purity and
   concentration were determined using a spectrophotometer (BioPhotometer,
   Eppendorf). Only the samples with a purity of ≥1.6 were used. One
   hundred nanograms of RNA was loaded for hybridization with nCounter^®
   PanCancer Progression Panel, according to the manufacturer’s
   instructions (NanoString, Seattle, WA, USA). The hybridization signals
   were analyzed by the nCounter^® FLEX Analysis System (NanoString).

   Raw count data, which include 740 endogenous genes, 6 positive
   controls, 8 negative controls, and 30 housekeeping genes, were
   preprocessed using the R package NanoStringNorm^[164]67. Raw counts
   were merged by patients by taking an average of samples. Specifically,
   geometric mean-based normalization was used to normalize for technical
   assay variation, followed by background adjustment based on negative
   controls, where “mean.2sd” is used. RNA content normalization was
   performed by all 30 housekeeping genes with the parameter
   “housekeeping.geo.mean”. Finally, log2-transformed data were used for
   downstream analysis. One sample was excluded for the analysis due to
   the high positive normalization factor examined by
   NanoStringNorm^[165]67. Differential expression analysis was performed
   using the R package Limma^[166]60.

Statistical analysis

   The statistical significance of CYT differences between group A and
   other tumors was evaluated by a two-tailed Student t test in the R
   package.

Ethical approval

   This study was declared to and approved by the French National
   Commission for Informatics and Freedom (Commission Nationale de
   l’Informatique et des Libertés), under number 1209138, as well as by
   the Ethical Committee of Clermont Ferrand (numbers AU 806 and 711).
   Each patient signed an informed consent presenting details of the
   study.

Reporting summary

   Further information on research design is available in the [167]Nature
   Research Reporting Summary linked to this article.

Supplementary information

   [168]Supplementary Information^ (876.8KB, pdf)
   [169]Reporting Summary^ (72.1KB, pdf)

Acknowledgements