The Residual Cancer Burden index as a valid prognostic indicator in breast cancer after Neoadjuvant chemotherapy

doi:10.21203/rs.3.rs-2730771/v1

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The Residual Cancer Burden index as a valid prognostic indicator in breast cancer after Neoadjuvant chemotherapy

https://doi.org/10.21203/rs.3.rs-2730771/v1

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Purpose The residual cancer burden index (RCB) was proposed as a response evaluation criterion in breast cancer patients treated with Neoadjuvant chemotherapy (NAC). This study evaluated the relevance of RCB with replase-free survival (RFS).

MethodsThe clinical data of 254 breast cancer patients who received NAC between 2016 and 2020 were retrospectively collected. The relationship between clinicopathologic factors and RFS was evaluated using Cox proportional hazards regression models. RFS estimates were determined by Kaplan-Meier(K-M) analysis and compared using the log-rank test. Multivariate logistic regression analysis was used to evaluate the risk factors associated with RCB 0.Receiver operating characteristic (ROC) curves showed the potential of the RCB and MP grading systems as biomarkers for RFS.

Results At a median follow-up of 26 months, 42 patients(16.5%) developed relapse. Multivariate Cox regression showed that older age (P=0.007), high Ki-67 expression (P=0.015) and a high RCB score(P=0.001) were risk factors for relapse. The outcomes of the multivariate logistic analysis indicated that RCB 0 (pathologic complete response [pCR]) was associated with HER-2-positive patients (P=0.002) and triple-negative breast cancer (TNBC) patients (P=0.009).In addition, the RCB and MP scoring systems served as prognostic markers for patients who received NAC, and their areas under the curve (AUCs) were 0.711 and 0.682, respectively.

Conclusion These data suggest that RCB can be equally applied to predict RFS in Chinese patients with NAC, which may guide the selection of treatment strategies.

residual cancer burden

neoadjuvant chemotherapy

breast cancer

pathologic complete response

Miller-Payne grading

The prevalence of breast cancer has continuously increased worldwide over the past few decades, with a particularly drastic increase in developing countries[1, 2].Breast cancer has emerged as the most prevalent female malignant neoplasia, with morbidity predicted to continue to increase in the future[3]. In China, it was estimated that breast cancer caused 70000 deaths in 2015,with nearly 270,000 new patients with breast cancer estimated for that year alone[4, 5]. Although advancements have been made in early detection and aggressive treatment in recent decades, the health quality and prognosis of breast cancer patients remain poor.

Currently, Neoadjuvant chemotherapy (NAC) is recognized as an indispensable treatment option for high-risk breast cancer[6, 7].The principal role of NAC is to downstage cancers, so that inoperable patients can undergo surgery, or patients who are not suitable for breast-conserving surgery (BCS) can obtain breast-conserving opportunities to satisfy their aesthetic needs. In addition, the response to NAC can be used to evaluate drug sensitivity and guide follow-up adjuvant therapy[8-10]. Patients who achieve pathologic complete response (pCR) after NAC may have a long-term survival benefit, although differences have been observed among breast cancer subtypes[11]. Many breast cancer patients who cannot reach pCR after NAC, and the prognosis of non-pCR patients greatly varies [12, 13].Therefore, to maximize the effect of neoadjuvant therapy regimens in distinct breast cancer subtypes, a more precise pathological evaluation system is urgently required to guide clinicians to develop personalized treatment protocols and improve the prognosis of patients.

The residual cancer burden (RCB) index is a scoring system for assessing residual lesions after NAC based on breast tumor bed tumors and regional lymph nodes proposed in 2007[14]. Subsequently, much clinical evidence from America and Europe has proven that the RCB system is effective, repeatable and useful for the pathological evaluation of different subtypes of breast cancer after treatment[15-18]. The RCB system is perceived as a long-term prognostic iNATrument and has been demoNATrated to be a better predictor of overall survival than most evaluation systems[19].In recent years, the RCB system has been gradually recognized in Asia, and the 2021 version of the Chinese Society of Clinical Oncology (CSCO) guidelines added the RCB index as a post-NAT evaluation system. Our research mainly aimed to validate the contact between the RCB score and prognosis in the Chinese population by analyzing real-world data.

Patients and data collection

In this retrospective study, we included breast cancer patients who received NAC

at Shandong Cancer Hospital and INATitute and Liaocheng Peoples Hospital between 2016 and 2020."Neoadjuvant chemotherapy" was used as the appropriate keyword to search for breast cancer patients via computers. We excluded patients who did not receive surgery after NAC. Among these patients,223 (87.8%) underwent radical mastectomy, and 31 (12.2%) underwent BCS. All of these patients had completed radiotherapy after BCS. We also gathered clinicopathological data, including the onset age, meNATrual status, clinical stage, pretreatment estrogen receptor (ER) and progesterone receptor (PR) levels and human epidermal growth factor receptor 2 (HER2) and Ki-67 statuses, type of operation, posttreatment T stage and N stage, presence of lymphatic vessel invasion (LVI), chemotherapy regimen, targeted therapeutic options, and Miller-Payne grade.

The enrolled patients were classified according to ER,PR and HER2 status as follows: ER-positive or PR-positive and HER2-negative was defined as HR+/HER2-; ER-positive or PR-positive and HER2-positive was defined as HR+/HER2+; ER- negative, PR-negative, and HER2-positive was defined as HR-/HER2+; ER-negative ,PR-negative, and HER2-positive was defined as triple-negative breast cancer (TNBC).ER and PR were positively stained in at least 1% of nuclei. HER2 positivity was defined as an immunohistochemistry score of 3+ or 2+ with HER2 gene amplification by fluorescence in situ hybridization.

Miller-Payne grading system:

The Miller-Payne grading system is routinely used by the two hospitals to assess the pathologic response after NAC. The criteria of classification were as follows.

Grade 1: No change or some alteration to individual malignant cells but no reduction in overall cellularity.

Grade 2: A minor loss of tumor cells(up to 30% loss),but high overall cellularity.

Grade 3: Estimated 30-90% reduction in tumor cells.

Grade 4: A marked disappearance of tumor cells (more than 90% loss of tumor cells) such that only small clusters or widely dispersed individual cells remain.

Grade 5: No malignant cells identifiable in sections from the site of the tumor; only vascular fibroelastotic stroma containing macrophages often remains. However, ductal carcinoma in situ (DCIS) may be present.

RCB score calculation:

Neither of the two hospitals routinely evaluated the pathology by the RCB system. Thus, two pathologists from Shandong Cancer Hospital reevaluated the postoperative pathology of the 254 patients according to the requirements of the RCB evaluation system and input the data into the network calculator (www.mdanderson.org/breastCancer_RCB) to calculate the RCB index. Then, according to the cutoff values of 1.36 and 3.28, the patients were further categorized into four different RCB classes: RCB 0 (equal to pCR), RCB I, RCB II and RCB III.

Routine survival tracking:

The patients were followed up for a long time via outpatient reexamination, telephone and e-mail. All cases received a standard postsurgical records, with scheduled clinical visits and imaging examinations every 3 months during the first year, every 6 months during the subsequent 2 years, and once yearly thereafter.

The primary follow-up endpoint was RFS, with the interval from the operation to the first occurrence of disease relapse and distant metastasis.

Statistical analysis:

The data analysis was performed with SPSS V.25 and GraphPad Prism 8.0.2.The clinically significant pathological features were screened via a Cox regression model, and the log-rank test was performed. Logistic regression was used to identify the factors associated with pCR. GraphPad Prism 8.0.2 was used to draw the survival curves of RFS and pCR. The diagnostic efficiency was judged by the receiver operating characteristic (ROC) curve, including the area under the curve (AUC), specificity and sensitivity. P≤0.05 was defined as statistically significant.

Baseline features:

In all, 254 patients with a median follow-up of 26 months were enrolled. A total of 42 patients (16.5%) developed recurrence (Figure 1 for recurrence survival curve), including local recurrence in 7 patients (2.8%) and distant metastasis in 35 patients (13.8%), and 1 patient died of brain metastasis.

The median age at the first diagnosis was 49 years (28 to 71 years),with 102 patients (40.2%) being more than 50 years old at the first diagnosis. The clinical stages of all the patients were II or III: 125 patients (49.2%) were stage II, and 129 (50.8%) patients were stage III. The percentage of premenopausal women at first diagnosis was 61.4%. At pretreatment, estrogen receptor(ER)-positive patients accounted for 60.2% (153/254), 116 patients (45.7%) were hormone receptor (HR)-positive and 99 patients (39.0%) were HER-2-positive.All the patients received neoadjuvant chemotherapy and were divided into three cohorts: 14 patients (5.5%) were treated with accepted anthracyclines only, 20 patients (7.9%) were treated with accepted taxanes only, and 220 patients (86.6%) were treated with anthracycline and taxane combinations. Among 99 the patients who were HER-2-positive, 44 (44.4%) were administered anti-HER2 treatment using trastuzumab, 14 patients (14.1%) selected dual-targeting therapy, and an additional 41 patients (41.4%) rejected receiving any targeted therapies. The RCB scores were judged by two expert pathologists. A total of 59 patients(23.2%) were categorized as RCB 0 (pCR), 33(13.0%) as RCB I, 91(35.8%) as RCB II and 71(28.0%) as RCB III. The patients were divided into four types according to the HR status of preoperative biopsy: 107 patients (42.1%) had HR+/HER2- breast cancer, 46 patients (18.1%) had HR+/HER2+ breast cancer,53 patients (20.9%) had HR-/HER2+ breast cancer, and 48 patients (18.9%) had TNBC( as shown in Table 1).

Correlation of factors with RFS:

The statistical results in Table 2 show that the age at the first diagnosis(P=0.019), clinical stage (P=0.039), Ki-67 proliferation index (P=0.060), pathologic T stage after NAC (P=0.009), LVI (P=0.025),and RCB (P=0.003) were significant characteristics for RFS. Then, these 6 significant factors were included in the multivariate Cox regression model, and age (HR of >50 years vs. ≤50 years = 2.407; P= 0.007), high Ki-67 expression (HR of high vs. low =2.600; P= 0.015), and high RCB score(HR of RCB II vs. RCB 0=8.695, P=0.006; HR of RCB III vs. RCB0=14.330, P=0.001) were significant.

Kaplan-Meier (K-M) survival curves for RFS based on the age, Ki-67 index, and RCB score are displayed in Figure 2. The analysis suggest that age >50 years (Figure 2A), Ki-67 >20% (Figure 2B) and a high RCB score(Figure 2C) were significantly correlated with a shorter time to relapse. Notably, in the risk assessment, there was apparently no difference in RCB I compared with RCB 0 (P=0.327, Table 2).

Correlation of factors with pCR:

Among 254 patients, 59 (23.2%) achieved pCR (breast and axilla negative). To assess the association between pCR and clinicopathologic factors, we conducted logistic regression analysis. In a multivariate model, significant predictors of pCR included the TNBC, and HER2+ subtypes. In total, 86.6% of cases received an anthracycline/taxane-based regimen with no association found between pCR and the type of chemotherapy (P=0.310, Table 3).

RCB reclassification based on different prognoses:

According to the K-M curve of RCB for RFS, we classified RCB into two categories: RCB 0/RCB I with good prognosis was denoted as RCBw, and RCB II/RCB III with poor prognosis was denoted as RCBb. To further verify the predictive effect of RCB in each molecular type, we carried out K-M survival curve analysis. The results are shown in Figure 3. In HR+/HER2- and HR-/HER2+ types, the prognosis of RCB was worse than that of RCBw(p=0.05; P=0.02, respectively). However, there were no statistical differences in the HR+/HER2+ and TNBC subtypes, with P=0.07 and P=0.09, respectively, which were very close to the threshold of 0.05 and suggest that if we enlarged the sample size, there were significant differences in all subtypes.

ROC curves for RCB and Miller-Payne grade:

To verify the predictive effect of the RCB and Miller-Payne systems on RFS after NAC, we performed an ROC curve analysis, which show that the AUCs of the RCB and Miller-Payne systems were 0. 711 and 0.682, respectively. This result indicates that both of them are effective evaluation systems for RFS, but the specificity and sensitivity of the RCB score to RFS were stronger than those of the Miller-Payne P grading system (Figure 4).

Breast cancer includes multiple molecular subtypes and is a highly heterogeneous solid tumor[20]. Neoadjuvant therapy has been proven to increase the radical resection rate and breast preservation rate. The treatment efficacy varies from person to person, and the clinical response is a method of early evaluation[21, 22].In this paper, we emphatically analyzed the viability of the RCB model and its influence on the prognosis after NAC. Our cohort was a collection of high-risk cases (Table 1: TNBC 18.9% and HER2-positive 20.9%), and the distribution of these subtypes was similar to that in the RCB validation groups performed by Symmans et al.[14, 16]. Although the research has some shortcomings, we found that the survival prediction of RCB was similar to that in previous studies.

The Miller-Payne grading system is an accepted model that compares preoperative and postoperative tumor tissues, and is extensively used in neoadjuvant efficacy evaluation in domestic hospitals[23]. According to the percentage of cell density reduction in primary tumor foci, the system categorizes NAT efficacy from class 1 to class 5[24]. Although it concisely and visually depicts the critical parameters associated with breast carcinomas and guides the selection of subsequent clinical treatment, it does not meticulously assess the postoperative pathology, particularly in patients with lymph node metastasis. And the MP system is not sufficiently comprehensive to measure the curative effect of tumor treatment due to the evaluation of only primary breast lesions. Moreover, following effective neoadjuvant therapy for smaller tumors, the decrease in tumor cell density is more obvious than that in larger tumors, which indicates that the change in tumor cell density alone is not sufficiently comprehensive and objective to evaluate the therapeutic effect of tumor treatment[25]. In contrast, the RCB system has more meticulous requirements for specimen collection and microscopic evaluation after neoadjuvant therapy. The RCB score contains information on the tumor foci and positive lymph nodes. The long and short diameters of the tumor foci, number of positive lymph nodes, proportion of the primary tumor beds that contain infiltrating cells and maximum diameter of the axillary lymph node metastasis are used to calculate the score after the NAC treatment [26]. And this system has been gradually recognized in China over the years.

As an effective postoperative pathological response evaluation system, RCB has been validated in many Western countries and regions. A classic clinical test (protocol MDACC-LAB98–240), with the longest cohort follow-up time of 13 years, revealed no difference in survival between low RCB grades; in contrast, poor prognosis was mainly associated with the higher RCB class, which was assessed by Symmans et al.[16]. This conclusion was validated in another study by Müller, H. D et al. ,who enrolled 184 cases[15]. In our retrospective study, we found that patients classified as RCB I (HR of RCB I vs. RCB 0=2.714, p=0.327) could have a good prognosis and a low risk of recurrence since these patients achieved pCR. As expected, patients with a higher RCB class had worse survival outcomes, as confirmed by the Cox multivariate analysis, where RCB II (HR of RCB II vs. RCB 0=8.695, p=0.006) and RCB III (HR of RCB III vs. RCB 0=14.330,p=0.001) were significant factors. The results are consistent with those obtained by others. Ki-67 represents cell proliferation and is a recognized risk factor in breast cancer patients[27, 28]. Our study show that patients had a shorter RFS with Ki67 >20%, which suggest that lesions with high proliferative capacity may have worse outcomes. The results are also consistent with findings in other studies[29, 30].

Young age is a known risk factor for long-term survival in patients who undergo breast-conserving surgery and are not treated with NAC[31, 32]. This view was verified by a meta-analysis of large-scale prospective tests of breast-conserving surgery, which suggested that younger female patients had a higher 10-year locoregional recurrence rate(LRR)[33]. Nevertheless, once the patients were treated with NAC, we could not able to assess the role of age in predicting survival outcomes. A large and authoritative EORTC 10994/BIG 1–00 study showed that younger age was not a risk factor for local recurrence(LR)[34], and another study by Müller, H. D et al. from Europe did not separately analyze the age[15]. Another study included 263 cases, with a cutoff value of 50 years, and mainly analyzed the impact of younger age on LR after NAC. The results revealed that patients <50 years could have higher pCR rates, and young age could have a better outcome after NAC[35]. Our study divided the cases by age into two sets, with 102 patients (40.2%)>50 years, and we concluded that older age (>50 years) would have a higher rate of relapse; however, we have no evidence to verify that younger age was highly predictive of pCR.

Our binary logistic regression analysis reveal that the phenotypic subtype was the unique associated factor in models that included age, stage, and chemotherapy regimens, and we found that patients with HER2-positive breast cancer, particularly TNBC, had higher pCR rates than HR-positive/HER2-negative patients. Similar conclusions have been observed in other studies[36, 37]. Increased RFS with pCR occurred regardless of the clinicopathological characteristics, including HR-positive/HER2-negative patients[38]. Finally, ROC curves were used to evaluate the prognostic efficiency of the RCB and Miller-Payne scoring systems for RFS, including calculation of the AUC, which demoNATrated the favorable diagnostic efficiencies of the RCB and Miller-Payne scoring systems, with AUCs of 0.711 and 0.682, respectively. Taken together, these data suggest that the two systems are promising predictors for breast cancer patients treated with NAC.

Some disadvantages remain in our study. The leading is that we had a short follow-up time for these patients and were unable to obtain the overall survival data. In the future, we will continue to expand the number of case samples and increase the follow-up time to obtain more convincing survival data. Second, our study may have introduced selection bias because the data came from only two hospitals. Last, due to the diversity of neoadjuvant chemotherapy regimens, 86.6% of cases had received the same kind of chemotherapy regimen.

Although the RCB system has more detailed requirements for evaluation, it can only be used to evaluate postoperative pathology. The cell density of thick-needle aspiration specimens before neoadjuvant therapy and surgical specimens after neoadjuvant therapy can’t be compared as in the Miller-Payne system and can’t reflect the contrast gap before and after neoadjuvant therapy, so it also has some limitations. Sheri et al. combined RCB score with the Ki-67 to form a "residual proliferative tumor load" (residual proliferative cancer burden, RPCB) system, and the RPCB score provided richer prognostic information and had a higher predictive efficiency[39]. Recent studies have combined tumor infiltrating lymphocytes (TILs) with the RCB score as an original evaluation system, particularly in TNBC, which also shows good prospects[40, 41].

Our team analyzed the differentially expressed genes of the resected tissue following NAT, and it is believed that promising biomarkers with prognostic value will be found soon. In addition, Shandong Cancer Hospital took the lead in performing internal breast lymph node biopsy in China[42-44]. We also envision combining the internal breast lymph node information with the RCB system to develop a new, more comprehensive and accurate postoperative pathological evaluation system.

Overall, the evaluation of pathologic response is immensely valuable because it provides a reliable supplement to pretreatment clinical and pathologic information. The RCB scoring system serves as an outstanding instrument to help us identify patients who are candidates for post-neoadjuvant clinical studies. We accomplished our research objective by proving that the RCB system as a pathological evaluation system after NAC is also applicable in routine clinical settings.

Funding: This work was funded by the Grants from the Natural Science Foundation of Shandong Province (ZR2019MH109).

Competing Interests: The authors have no relevant financial or non-financial interests to disclose.

Author Contributions: All authors contributed to the study conception and design. Wei Zhao analyzed the data and wrote the manuscript. Zhaoyun Liu collected the data, analyzed the data and edited the manuscript. Zhiyong Yu played a role in developing the idea. All authors agree with the order of presentation of the authors. All authors have read and agreed to the published version of the manuscript.

Data Availability: The datasets are available upon reasonable request to the corresponding author.

Ethics approval: The study was reviewed and approved by the Ethics Committee and Institutional Review Boards in the Shandong Cancer Hospital and Institute. The approval No. SDTHEC 201802002.

Consent to participate: Informed consent was obtained from all individual participants included in the study. All authors would like to thank the patients who agree us to collect medical records.

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Table 1 Baseline characteristics of patients:

Table 1 Patient Characteristics

Factors

Percentage(%)

Overall

254

100.0

RFS event

16.54

Age at diagnosis

≤50

＞50

152

102

59.8

40.2

Menopausal status

premenopausal

postmenopausal

156

61.4

38.6

Clinical stage

III

125

129

49.2

50.8

Estrogen receptor

Negative

Positive

101

153

39.8

60.2

Progesterone receptor

Negative

Positive

138

116

54.3

45.7

HER2 status

Negative

Positive

155

61.0

39.0

Ki-67 proliferation index

≤20

＞20

169

33.5

66.5

Receptor status

HR+/HER2–

HR+/HER2+

HR-/HER2+

TNBC

107

42.1

18.1

20.9

18.9

Type of breast surgery

Mastectomy

Breast conservation

223

87.8

12.2

Pathologic T stage after NAT

ypT0

ypT1

ypT2

ypT3

ypT4

122

48.0

30.7

18.9

0.8

1.6

Pathologic N stage after NAT

ypN0

ypN1

ypN2

ypN3

118

46.5

29.1

17.3

7.1

lymphatic vessel invasion(LVI)

Negative

Positive

217

85.4

14.6

Neoadjuvant chemotherapy regimen

Anthracycline

Taxane

Anthracycline+Taxane

220

5.5

7.9

86.6

Neoadjuvant anti-HER2 therapy regimen

reject

Single targeted drug

Double targeted drugs

Not applicable (HER2–)

155

16.1

17.3

5.5

61.0

MP system

7.5

22.8

20.5

17.7

31.5

RCB system

0(pCR)

III

23.2

13.0

35.8

28.0

Abbreviations: RFS, relapse-free survival; HER2,human epidermal growth factor receptor 2; HR, hormone receptor; TNBC, triple negative breast cancer; NAT, neoadjuvant systemic therapy; MP, Miller-Payne; RCB, residual cancer burden; pCR, pathologic complete response

Table 2 Analysis of correlation factors with RFS

Table 2

RFS

Factors

Univariate analysis

Multivariate analysis

All patients(n=254)

95% CI

P Value

95% CI

P Value

Age at diagnosis

≤50

＞50

2.081

1.129-3.836

0.019

2.407

1.269-4.563

0.007

Clinical stage

III

1.999

1.036-3.855

0.039

1.177

0.584-2.374

0.648

Ki-67 proliferation index

≤20

＞20

2.029

0.970-4.244

0.060

2.600

1.202-5.621

0.015

Pathologic T stage after NAT

ypT0

ypT1

ypT2

ypT3

ypT4

1.218

1.604

6.196

8.271

0.570-2.606

0.725-3.547

0.810-47.412

2.362-28.962

0.009

0.611

0.243

0.079

0.001

0.642

0.649

0.706

3.363

0.284-1.453

0.267-1.580

0.080-6.220

0.837-13.520

0.134

0.288

0.341

0.754

0.088

LVI

Negative

Positive

2.196

1.103-4.370

0.025

1.491

0.723-3.075

0.279

RCB system

III

1.673

5.317

9.293

0.235-11.869

1.222-23.142

2.184-39.541

0.003

0.607

0.026

0.003

2.714

8.695

14.330

0.368-20.004

1.857-40.718

2.910-70.559

0.004

0.327

0.006

0.001

Abbreviations: LVI, lymphatic vessel invasion

Table 3 Association of Factors with pCR

Table 3	pCR
Correlation of factors with pCR	Univariate analysis			Multivariate analysis
Factors	OR	95% CI	P Value	OR	95% CI	P Value
Age at diagnosis (＞50 years)	1.127	0.624-2.034	0.692	1.007	0.540-1.878	0.982
Clinical stage III vs II	0.704	0.392-1.264	0.240	0.720	0.388-1.333	0.296
HR+/HER2+vs HR+/HER2–	1.845	0.752-4.530	0.181	1.858	0.748-4.615	0.182
HER2+ vs HR+/HER2–	4.026	1.827-8.871	0.001	3.608	1.580-8.239	0.002
TNBC vs HR+/HER2–	3.019	1.317-6.922	0.009	2.890	1.253-6.665	0.013
Anthracycline+ taxane vs other regimens	0.498	0.230-1.080	0.077	0.646	0.277-1.504	0.310

No competing interests reported.

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The Residual Cancer Burden index as a valid prognostic indicator in breast cancer after Neoadjuvant chemotherapy

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