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Evolution of synchronous female bilateral breast cancers and response to treatment

Issuing time:2023-03-15 14:31

Abstract

Synchronous bilateral breast cancer (sBBC) occurs after both breasts have been affected by the same germline genetics and environmental exposures. Little evidence exists regarding immune infiltration and response to treatment in sBBCs. Here we show that the impact of the subtype of breast cancer on levels of tumor infiltrating lymphocytes (TILs, n = 277) and on pathologic complete response (pCR) rates (n = 140) differed according to the concordant or discordant subtype of breast cancer of the contralateral tumor: luminal breast tumors with a discordant contralateral tumor had higher TIL levels and higher pCR rates than those with a concordant contralateral tumor. Tumor sequencing revealed that left and right tumors (n = 20) were independent regarding somatic mutations, copy number alterations and clonal phylogeny, whereas primary tumor and residual disease were closely related both from the somatic mutation and from the transcriptomic point of view. Our study indicates that tumor-intrinsic characteristics may have a role in the association of tumor immunity and pCR and demonstrates that the characteristics of the contralateral tumor are also associated with immune infiltration and response to treatment.

Main

Bilateral breast cancers (BBCs) represent 2–11% of breast cancers1,2,3, and their incidence is increasing owing to advances in breast cancer imaging4. This entity includes both synchronous bilateral breast cancers (sBBCs)—that is, occurring synchronously in both breasts—and metachronous bilateral breast cancers (mBBCs)—that is, a tumor occurring in the contralateral breast at a later time period from the primary index cancer. In several studies, sBBCs are associated with poorer survival than unilateral cancer2,5,6. Neither synchronous nor metachronous breast cancer is associated with strong genetic determinants, and only 5% of patients with BBC carry BRCA1 or BRCA2 mutations5.

From the genomic point of view, several studies7,8,9,10,11,12,13,14, although with old technologies, investigated clonal relationships among BBCs, with most reaching the conclusion that most, if not all, of BBCs were independent events7,9,10,13,15,16,17. Recently, analyzing a targeted sequencing panel of 254 genes, Begg et al.18 investigated the clonality of BBC pairs and found that only one pair of 39 sBBCs was interpreted as clonally related (two shared mutations out of three mutations identified), leaving the question of the independence among sBBCs unresolved.

The immune microenvironment, and especially the role of tumor-infiltrating lymphocytes (TILs), in breast cancer has been studied extensively in the last decade. The drivers of the immunosurveillance of breast cancer derive from both (1) tumor-intrinsic characteristics, such as the subtype of breast cancer, proliferative patterns and tumor mutational burden19, and/or (2) extrinsic factors related to the host (for example, sex20, age21 and body mass index) or the environment (for example, tobacco, alcohol and commensal microbiota). It remains unclear to what extent anti-tumor immunity is driven by the tumor, by the host and/or by the interaction between the host and the tumor.

Neoadjuvant chemotherapy (NAC) is currently administered to patients with locally advanced breast cancers. Molecular subtypes of breast cancers and the density of tumor-infiltrating immune cells are both considered as important predictive and prognostic factors. Many studies have reported associations between high levels of TILs at diagnosis and better response to NAC22,23 as well as better prognosis24.

sBBCs occur after both breasts have been affected by the same germline genetics, reproductive life factors and environmental exposures for several decades. Two tumors arising concomitantly in a host mimic a model where (1) extrinsic factors are almost fully shared by the same host; (2) intrinsic factors are specific to the tumor of each side; and (3) the immune tumoral microenvironment resulting from the interaction between the same host and two different tumors can be compared.

In the current study, we identified a rare resource of 20 tumors deriving from six patients with sBBCs treated by NAC with left and right pre-NAC and post-NAC with frozen material available. We performed whole-exome sequencing (WES), copy number alterations (CNAs) and RNA sequencing (RNA-seq) to comprehensively analyze somatic alterations, the immune microenvironment and the tumor evolution under treatment.

Results

Patient and tumor characteristics

Out of 17,575 patients with breast cancer in our institutional clinical database, 404 patients had sBBCs (2.3%) (Extended Data Fig. 1). Slight differences existed in patient and tumor characteristics between patients with unilateral breast cancers and patients with sBBCs (Supplementary Table 1 and Supplementary Fig. 1). Out of 313 patients with invasive sBBCs, most of the tumors were luminal (n = 538, 87.6%), whereas triple-negative breast cancer (TNBC) (n = 44, 7.2%) and HER2+ breast cancers (n = 32, 5.2%) were rare (Supplementary Table 2). Only 13 patients were carriers of a genetic germline BRCA1 or BRCA2 predisposition. They were significantly younger and more likely to be diagnosed with large, palpable and high-grade tumors, more frequently of TNBC subtype (Supplementary Table 3).

Concordance of sBBCs

Overall, the 313 paired invasive sBBC tumors shared more common characteristics than expected by chance (Supplementary Table 4): most (84.7%) of the tumor pairs were concordant regarding clinical and pathological patterns, notably regarding the subtype of breast cancer (Fig. 1a). A minority of pairs of tumors belonged to different breast cancer subtypes (discordant pairs: 15.3%), and both the proportion of pairs (18%) and their relative repartition were similar in the validation cohort of 8,367 patients with sBBCs from the Surveillance, Epidemiology, and End Results (SEER) database (Fig. 1b).

a, Repartition of the association of the subtypes of breast cancers within a pair of sBBCs according to the concordance or the discordance status of the pair in the Curie cohort; tumor characteristics are based on the 302 pairs with concordance subtype available out of 313 pairs. The concordance refers to the status of both tumors within a pair of sBBCs regarding the subtype of breast cancer, either of the same subtype of breast cancer (tumor in concordant pair) or of different subtypes of breast cancers (tumor in discordant pair). b, Repartition of the association of the subtypes of breast cancers within a pair of sBBCs according to the concordance or the discordance status of the pair in the SEER cohort (8,367 patients with sBBCs, n = 16,734 tumors). c, Stromal TIL levels of the index tumor by subtype of breast cancer and by the concordance status of the pair it belongs to. Lower and upper bars represent the first and third quartiles, respectively; the medium bar is the median; and whiskers extend to 1.5 times the IQR. d, Intratumoral TIL levels of the index tumor by subtype of breast cancer and by the concordance status of the pair it belongs to. Lower and upper bars represent the first and third quartiles, respectively; the medium bar is the median; and whiskers extend to 1.5 times the IQR. e, pCR rates of the index tumor by subtype of breast cancer and by the concordance status of the pair it belongs to. f, Axillar pCR rates (proportion of patients with a post-NAC number of positive nodes >1 divided by the total number of patients) of the index tumor by subtype of breast cancer and by the concordance status of the pair it belongs to (n = 467 patients treated with NAC, representing 934 tumors). Statistical tests were Wilcoxon tests (c,d), Fisher tests (e) and chi-square tests (f). IT, intratumoral; Str, stromal.

Baseline immune infiltration and variation after neoadjuvant treatment

Immune infiltration levels before treatment were assessed by the presence of a mononuclear cell infiltrate, following the recommendations of the international TILs Working Group25,26, on hematoxylin-and-eosin (H&E)-stained sections in 149 patients (277 tumors). The difference between the TIL levels from the left and the right tumor was higher in pairs of discordant subtypes of breast cancers than in pairs of concordant subtypes (Extended Data Fig. 2). At the tumor level, TIL levels were independently associated with higher-grade tumors (Supplementary Tables 5 and 6), and, interestingly, the relationship between TIL levels and the subtype of breast cancer showed a systemic effect—that is, it was affected by the subtype of the contralateral tumor. In luminal breast cancers, stromal TIL levels were lower when the subtype of the contralateral tumor was concordant than when it was discordant, and the same trend was observed for intratumoral TILs (Fig. 1c,d). Conversely, in TNBCs, the intratumoral TIL levels were lower when the subtype of the contralateral tumor was concordant than when it was discordant. The interaction test was highly significant (Pinteraction = 0.0006), indicating that the impact of tumor subtype on intratumoral immune infiltration was significantly modified by the concordance of the subtype of breast cancer of the tumor pair it belonged to. This result was also validated in a third independent cohort from the German Breast Group (GBG), where the interaction tests were highly significant both for stromal and intratumoral TILs (Pinteraction = 0.007 and Pinteraction = 0.006 respectively) (Supplementary Fig. 2). This suggests that TIL levels are not determined purely by local tumor microenvironment properties.

On paired pre-neoadjuvant treatment (NAT) and post-NAT data on immune infiltration available for 74 tumors (37 patients) (Extended Data Fig. 3), stromal TIL levels decreased in 30 tumors (40.5%), remained stable in 18 tumors (24.3%) and increased in 26 tumors (35.1%). The decrease of TIL levels was of larger magnitude in tumors belonging to discordant pairs, to higher tumor grade and with high pre-NAC stromal TIL levels, and, in case of treatment with NAC rather than NET, the TIL decrease was very strongly associated with the occurrence of a pathologic complete response (pCR) (Extended Data Fig. 4). As a whole, stromal TIL levels were not significantly different before and after NAT, but pre-NAT and post-NAT stromal TIL levels were significantly different according to the type of NAT in discordant, grade 3 tumors and in tumors that reached pCR (Supplementary Fig. 3). These findings suggest that NAT reshapes the immune contexture of sBBCs.

Response to NAT

Twenty-two tumors out of 140 tumors reached pCR. Pre-NAT stromal TIL levels and the subtype of breast cancer were independently associated with the occurrence of a pCR (Supplementary Table 7). As was seen for TIL levels, the pCR rates showed a systemic effect when the contralateral tumor subtype was discordant. In luminal breast cancers, the pCR rate was significantly higher when the contralateral pair was of discordant subtype (22% versus 6%), whereas no such pattern was found in the other subtypes (Pinteraction = 0.03) (Fig. 1e). Similar results were found in two independent validation cohorts. In the SEER validation cohort, the difference in the rate of axillar pCR in tumors belonging to discordant pairs versus in tumors belonging to concordant pairs was highly significant (68% versus 47%, P = 0.00001, respectively) (Fig. 1f). In the GBG cohort, the pCR rate in luminal breast cancers was significantly higher in tumors belonging to discordant pairs versus in tumors belonging to concordant pairs (30% versus 6%, P = 0.0002, respectively) (Supplementary Fig. 4). Survival analyses showed that clinical T stage, breast cancer subtype and tumor grade were significantly associated with relapse-free survival (RFS) (Supplementary Table 8).

Genome-scale analyses on pre-NAC and post-NAC samples from six patients

Of 50 patients with sBBCs treated with NAC, frozen material of sufficient quality was available in six patients to perform tumor/normal WES and tumor RNA-seq in both left and right pre-NAC and post-NAC samples (including one patient with a multicentric bilateral breast cancer) (Extended Data Fig. 5 and Supplementary Table 9). The total number of samples was 20 (pre-NAC: n = 14; post-NAC: n = 6), and this cohort was further used for all the genome-scale analyses, both at the DNA level and the RNA level. Germline pathogenic mutations in breast cancer predisposition genes were identified in four patients (BRCA1, n = 2; BRCA2, n = 2). Among the 14 primary tumors (PTs), nine were of luminal subtype, and five were TNBCs. All patients received standard sequential anthracyclines/cyclophosphamide followed by taxanes. After NAC completion, six out of 14 tumors had residual disease (RD), whereas eight tumors reached pCR.

Somatic single-nucleotide indel mutations

Twenty tumor samples were profiled by WES and RNA-seq, and distant juxta-tumor samples were used for germline WES sequencing in each patient. A median of 151.5 somatic mutations were detected per tumor, and a median of three mutations were annotated as potential drivers in each sample (Supplementary Table 10). No mutation was shared between the left and right side of the PTs from any patient, consistent with the contralateral tumors developing from independent clones (Fig. 2). Most of the mutations were shared between a PT sample and the matched RD.

The analyses were performed in the left, right, pre-NAC (PT) and post-NAC (RD) samples of a cohort of six patients (20 samples) with next-generation sequencing data available. a, Heat map of somatic driver mutations (including missense, nonsense and splicing) detected in six study patients and 20 samples. b, Venn diagrams showing the number of mutations shared between the left (pink) and the right (purple) primary tumors of a same patient and shared (intersect turquoise) between the PT (light green) and the corresponding specimen after NAC (RD, blue). The post-NAC samples RD4_R and RD6B_R were discarded owing to poor sample purity. Mutations from the two multicentric tumors of each side of patient 6 were merged. L, left; R, right; wt, wild-type.

Neoantigens

We predicted potential neoantigens from somatic mutations using netMHCpan27 after determining human leukocyte antigen (HLA) haplotypes with Seq2HLA28. Most of the antigens were predicted from HLA-C, and the repartition of predicted neoantigens was evenly distributed across patients (Supplementary Fig. 5a). The number of neoantigens was positively correlated with the levels of stromal TILs (Supplementary Fig. 5b), was not associated with the breast cancer subtype (Supplementary Fig. 5c) and was higher in PT samples than in RD samples (Supplementary Fig. 5d). No neoantigen was shared across patients, and no neoantigen was shared between the left and the right tumors. Conversely, the RD shared most of the mutations with the corresponding PT (Supplementary Fig. 5e).

CNAs

Copy number analysis of the WES profiles identified recurrent gains (eight out of 12 samples) at 1q, 8q and 17q and recurrent losses (over 50% of the samples) at 4p, 8p, 6q, 13q, 16q and, to a lesser extent, 1p (Supplementary fig. 6). Most of the alterations were not shared between the left and the right side (Fig. 3a), whereas most of the CNAs were consistent between PT and RD (Fig. 3B) (mean cosine distance 0.25 versus 0.75, P = 0.03; Supplementary Fig. 7).

The analyses are performed in the left, right, pre-NAC and post-NAC samples of a cohort of six patients (20 samples) with next-generation sequencing data available. Mutational signatures published by Alexandrov29 were calculated using the deconstructSigs package. a, CNAs are compared among the two sides of the primary tumor of the same patient. b, Among the primary tumor and its corresponding sample with RD after NAC. c, Mutational signatures are compared among the two sides of the primary tumor of the same patient (mutations from the two multicentric tumors of each side of patient 6 have been merged). d, Among the primary tumor and its corresponding sample with RD after NAC. CN-LOH, copy-neutral loss of heterozygosity.

Mutational signatures

We analyzed mutational signatures by deconvoluting the frequency of the 96 different possible trinucleotide substitutions against known signatures of mutation patterns29 (Fig. 3c). Similarities regarding mutational processes were lower within the left and right side of the PTs than within pairs of PT–RDs (Fig. 3d).

Clonality and phylogenetic evolution

We determined the phylogenetic evolution between the germline profile to the left and right primary tumors and ultimately to the RD if present (Fig. 4a–e). Genomic profiling found no common clones between bilateral PTs of the same patient, showing that these tumors arose through unrelated tumor evolution processes.

Each subfigure represents the evolution of the tumors of a given patient under NAC. The upper fish plot represents the evolution of the left tumor(s); the lower fish plot represents the evolution of the right tumor(s). Each fish plot displays the prevalence of subclones throughout treatment. Subclonal architecture was reconstructed with SuperFreq. Subclonal profiles show annotated common driver cancer genes. a, Patient 1: Both PTs were mutated for P53, but the genomic alteration was different on the left and the right side (right side, indel frameshift deletion position 7578213; left side, substitution C>T p.R175C missense substitution identified as pathogenic in ClinVar and present in the RD). b, Patient 2: Both tumors were mutated for TP53 with two different mutations (right side, frameshift loss of a nucleotide position 7577558). c, Patient 3. d, Patient 4: The right sample with RD (RD4_R) was discarded from analysis owing to low purity. e, Patient 5: TP53 was mutated on both sides (left side, frameshift deletion position 7578213; right side, frameshift deletion position 7579522).

Altogether, these results suggest that left and right PTs are not clonally related and that their evolution under NAC does not converge to a common profile. Hence, RD is closer to its associated PT than to its concomitant contralateral tumor.

Independent validation cohort of sBBCs for WES analyses

We performed WES analyses from an independent validation cohort of 14 sBBC samples treated by surgery as first treatment. Similar results were found regarding the genomic profiles of the left and right tumors: left and right tumors were found to be genomically independent in terms of mutations (Extended Data Fig. 6a,b), CNAs (Extended Data Fig. 6c), mutational signatures (Extended Data Fig. 6d) and phylogenetic evolution (Extended Data Fig. 6e).

Particular case of multicentric tumors

One patient had a bilateral multicentric tumor (patient 6) in the context of a BRCA2 pathogenic germline mutation. Although the left and the right tumors shared no common mutations, the two tumors from each side shared most genetic alterations at both the substitution (Fig. 5a) and copy number (Fig. 5b) levels as for mutation signature analyses (Fig. 5c). On each side, phylogenetic reconstruction clearly indicated that multicentric tumors were clonally related, with one tumor evoluting to a neighboor tumor through the extinction/emergence of particular subclones.

The analyses were performed in the two left tumors (PT6A_L and PT6B_L) and in the two right tumors (PT6A_R and PT6B_R) of the same patient (patient 6). The sample with RD (RD6B_R) was discarded owing to poor sample purity. a, Tumor mutation profiles: Venn diagrams showing the number of mutations shared between the two primary tumors of each side (yellow intersect). b, CNA profiles, compared between the two multicentric tumors of each side. c, Mutational signatures as from Alexandrov29 compared between the two multicentric tumors of each side. d, Fish plot retracing the phylogeny between the two multicentric tumors of each side. Each fish plot can be interpreted from the left to the right or from the right to the left, corresponding to the emergence or the extinction of a clone, respectively. CN-LOH, copy-neutral loss of heterozygosity.

Transcriptomic alterations

Tumor clustering and principal component analysis

We performed unsupervised hierarchical clustering based on transcriptomic profile of the most variable genes, and gene clustering split the 2,846 genes into four main clusters (Fig. 6a). The PT samples consistently clustered with their related RD rather than the tumor from the contralateral side. Similar results were seen after principal component analysis (PCA) using the 3,000 most variable genes (Fig. 6b). This suggests that PT and RD are closer from a transcriptomic point of view than are left and right tumors from the same patient.

a, Gene expression clustering with RNA-seq data based on the 2,846 most variant genes on the left, right, pre-NAC and post-NAC samples of a cohort of six patients (20 samples) with RNA-seq data available. Cluster 1 is enriched in genes coding for early and late response to estrogens; cluster 2 is enriched in genes coding for TNF signaling, myogenesis and epithelial–mesenchymal transition; cluster 3 is enriched in genes coding for G2M checkpoints, E2F targets and cellular cycle; and cluster 4 shows no clear enrichment in specific pathways. b, PCA using the 3,000 most variable genes. L, left; PC, principal component; R, right; wt, wild-type.

Qualitative immune infiltration analysis with deconvolution and T cell receptor sequencing analysis

We applied the CIBERSORT algorithm30 using the ‘absolute’ mode to deconvolute RNA-seq expression profiles into 22 subsets of immune subpopulations on the 20 samples of the cohort. The top three most abundant immune subpopulations were M2 macrophages, CD4 memory resting T cells and M1 macrophages (Supplementary Fig. 8a). CD4 memory T cells and M2 macrophages were increased in RD compared to PT (Supplementary Fig. 8b). Immunofluoresence was performed to evaluate the concordance between the immune subsets assessed by deconvolution and orthogonal techniques. The correlation coefficient between both metrics was statistically significant regarding cytotoxic T cells (CD8+ cells), Tregs (CD4+/FOXP3+ cells) and Mast cells resting (CKIT+/CK− cells) (Extended Data Fig. 7i,j,q); was nearly significant for T cells memory activated (CD45RO+ cells) and B cells naive (CD20+) (Extended Data Fig. 7k,l); and was not significant in macrophages M2 (CD68+/CD163+ cells) or macrophages of any type (CD68+ cells) and plasma cells (CD138+ cells) (Extended Data Fig. 7m–o). At the patient level, the immune composition of the paired contralateral tumors was different regarding several immune subsets (macrophages M0, M1 and M2 and T cells CD4 memory activated and resting) (Extended Data Fig. 8a), whereas the variation of the immune composition between PT and RD mostly concerned increasing levels of macrophages M2 and M0 and T cells CD4 memory resting (Extended Data Fig. 8b). We compared the predicted immune contexture patterns among samples of the cohort using a dissimilarity index (Idissimilarity). The higher the dissimilarity index, the more the composition of the immune infiltration differs. Neither the mean dissimilarity indices (Extended Data Fig. 9) of paired left and right tumor (green-bordered squares, mean Idissimilarity = 0.22) nor paired PT and RD (yellow-bordered squares, mean Idissimilarity = 0.29) were statistically different from the rest of the samples. At the cohort level, the dissimilarity was lower among the PT samples (blue area) than among the RD samples (orange area) (mean Idissimilarity: 0.24 versus 0.37, P = 0.009), whereas the greatest difference was seen between PT samples compared to RD samples (yellow area, mean Idissimilarity = 0.49). These results suggest that the composition of the immune microenvironment is strongly associated with the pretreated or non-pretreated character of the sample (PT or RD), in line with the changes in the immune contexture induced by NAC treatment.

T cell receptor sequencing analysis

To further investigate the T cell response to NAC and to compare infiltrating T cell receptor (TCR) repertoires across patients, we extracted TCR beta CDR3 sequences from the RNA-seq data using MixCR31 and immunarch32. The large majority of clonotypes retrieved were unique to a sample (Extended Data Fig. 10a), but some sequences were found in multiple samples. The proportion of samples annotated in VDJdb, a curated database of TCR sequences of known antigen specificity33, was low (1%) and was not different in sequences unique to a sample and in sequences shared within the cohort (8/638 versus 31/3,126, respectively, P = 0.7). We evaluated the diversity of the TCR repertoires using the Chao-1 estimator of species richness (Extended Data Fig. 10b–d) and the D50 diversity index (Extended Data Fig. 10e–g), and they were not different by breast cancer subtype nor PT or RD character of the sample. To measure repertoire similarity, we calculated the total number of shared clones between samples against ‘public’ clonotypes (Extended Data Fig. 10h). We found shared TCRs between individuals at a low frequency, whereas most common sharing relationships were found between PT and RD (yellow-bordered squares) and, to a smaller extent, between left and right tumors (green-bordered squares), although the median number of shared clonotypes was not statistically significant (20 versus 11, P = 0.12). Except for two samples that showed low sharing with any other sample (PT3_R and PT5_L), clonotypes of the same patients consistently clustered together, either with the contralateral side or with the corresponding RD/PT, consistent with a systemic effect of TILs (Extended Data Fig. 10i).

Discussion

In the current study, we conducted a large comprehensive overview of sBBC, integrating clinical and pathological data with immune infiltration and genomic profiles generated using modern WES and RNA-seq technologies. Our work thus provides important insights to understand the relationships among tumor, host, immunity and response to treatment.

First, sBBCs represent two distinct and independent diseases occurring incidentally at the same time. In line with previous studies34,35,36,37, we found a high concordance between the clinical and biological patterns within pairs of sBBCs. Here we demonstrate clearly that these tumors were genomically independent in terms of mutations, CNAs, expression patterns and clonal composition. This finding is in line with most published studies9,12,15,18. We also identified genomically related profiles in multicentric tumors, thus confirming that multi-focal tumors represent intra-mammary dissemination of a single breast cancer8,12,13,38. These results suggest that the occurrence of sBBCs is explained by non-genetic factors39, although very little data are currently available on the link between environmental factors and sBBCs.

Second, we found that the immune infiltration was not determined purely by local tumor microenvironment properties but was different according to the subtype of the contralateral tumor. Several hypotheses can be drawn to explain this observation. First, the immune system might be activated by an index tumor, and immune cells activated by this process might spread to the contralateral tumor. Second, as luminal breast cancers associated with a contralateral tumor of another subtype were associated with a lower degree of ‘luminalness’ (estrogen receptor (ER) and progesterone receptor (PR) staining), we cannot exclude that the highest immune infiltration is derived from such patterns rather than from the presence of the contralateral tumor.

Third, in luminal breast cancers, response to NAC was significantly higher in the case of discordant subtypes of contralateral tumor than in concordant pairs, as with TIL levels. Evidence regarding the influence of contralateral tumor on the response to treatment has not been described so far. Reinisch et al.40 previously reported that patients with BBCs had lower pCR rates than patients with unilateral breast cancer (12.6% versus 20.9%). After reanalyzing this dataset41,42,43,44, we validated independently the higher response rates in patients with discordant luminal breast cancers than in concordant cases, and this finding was also reproducible in a third validation cohort from the SEER program. Hypotheses explaining the difference of the rates of pCR according to the contralateral tumor could be the different baseline immune infiltration levels leading to an increased efficacy of the chemotherapy as previously described24 and/or changes in the chemosensitivity of an index tumor by the presence per se of a contralateral tumor. In addition, several other factors, such as different NAT regimen or the time length of treatment, could have modified response rates to NAT.

Finally, the TCR analyses identified that patient was the main source of variability of TCR, and TCRs were not differentially shared between pairs of left and right tumor than they were between pairs of PT–RDs. However, we cannot exclude that some sequences of TCR could have been missed due to the unspecificity of the whole-transcriptome approach against a specific CDR3 approach and due to the bulk tumor transcriptome analysis versus the identification of TCR repertoire specifically on TIL subsets. At a time where bilateral tumor contexts represent a model of growing interest to understand mechanisms underlying immune response to anti-cancer treatment in mice37,45, we provide human data regarding the temporal analysis of the TCR repertoire in sBBCs.

Our study has several strengths, such as the use of modern technologies. WES is more informative than targeted sequencing for determining clonal relationships. Second, we studied a very rare and unique cohort of patients, enabling direct comparison of left versus right PT together with a temporal analysis comparing paired samples before versus after NAC. Beyond the challenges in analyzing tumor evolution from bulk sequencing data, we were able to leverage multiple tumor samples to reconstruct a clonal phylogeny from germline data to left and right sBBCs both before and after treatment. Third, our data on immune infiltration are novel contributions to the literature and provide insights into the immune mechanisms underlying the biology of sBBCs.

This study also has limitations. We were able to sequence only a limited number of cases, and a subset of clonally related sBBCs could possibly be identified if larger cohorts were sequenced. Second, the cohort of patients with multiomics data was enriched in patients with BRCA mutations, and the latter might represent tumors with particular biological patterns. Third, characterization of the immune microenvironment by bulk sequencing approaches has inherent limitations and is hampered by the absence of ‘ground truth’ data. New insights could be generated by in situ single-cell technologies or through specific transcriptome of the CDR3 region or the TIL subset for analyzing TCR repertoire. Similarly, measurement error in the assays—notably to determine the subtype of breast cancer—could have modified some of our results, although the estimation of the proportion of errors was deemed not exceeding 2%. Finally, no formal causality can be inferred from human observational data, even though the findings of our studies were reproducible in independent validation cohorts.

To conclude, our data suggest that the similarity of molecular portraits in sBBCs could be influenced by common environmental factors and do not support the evidence of a common genetic clonal alteration. Both tumor immune infiltration and response to treatment are differentially associated with the subtype of breast cancer according to the concordant or discordant character of the contralateral tumor. Pairs of tumors from different subtypes of breast cancers should be considered as singular entities before primary systemic treatment is considered, as observed responses might deviate from well-known profiles of response to chemotherapy.

Article classification: Biological abstract

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