|Year : 2020 | Volume
| Issue : 5 | Page : 113-116
|Does tumor stroma ratio of breast cancer trucut biopsy determine response to neoadjuvant therapy?
Varuna Mallya1, Vishal Singh1, Navpreet Kaur1, Priyanka Yadav1, Shramana Mandal1, Nita Khurana1, Pawanindra Lal2
1 Department of Pathology, Maulana Azad Medical College, New Delhi, India
2 Department of Surgery, Maulana Azad Medical College, New Delhi, India
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|Date of Web Publication||26-Feb-2020|
| Abstract|| |
Aims and Objectives: We examined the prognostic value of Tumor stroma ratio (TSR) in breast tumor core biopsy (TCB) specimen to determine response to neoadjuvant therapy (NAT) prior to modified radical mastectomy (MRM). Methods: This was a retrospective analysis of patients with breast cancer who underwent TCB before NAT between August 2016 and July 2018. TSR in TCB was studied independently by 2 pathologists ( VM, VS) defined as stroma rich (TSR≤50%) or stroma poor (TSR>50%). MRM specimen of these patients were subsequently studied .Residual cancer burden (RCB) was calculated using the MD Anderson RCB calculator, categorized as complete (0), good (1) Partial (2) and no response (3). Statistical analysis was done to assess correlation of TSR to RCB. Results: A total of 62 patients were analyzed. Mean(SD) age was 48(11) years.Twenty eight (45%) and 34 (55%) patients were stroma rich and stroma poor respectively. Twenty six (42%) patients were responders and 36 (58%) non-responders to NAT. Among stroma rich patients, only 3 (10%) were responders (Class 0 &1)and 25 (90%) non-responders(Class2&3)to NAT, among stroma poor patients 23 (68%) responded well and 11 (32%) did not.TSR had a moderate negative correlation with RCB (-0.6). On univariate analysis, only TSR had a significant effect on RCB class (<0.001). Conclusions: TSR on TCB is a useful prognostic factor to determine response of breast carcinoma patients to neoadjuvant therapy.It is cost effective, simple and quick. Larger multi-centric studies would be useful to study its clinical implications.
Keywords: Breast, neoadjuvant chemotherapy, residual cancer burden, tumor stroma ratio
|How to cite this article:|
Mallya V, Singh V, Kaur N, Yadav P, Mandal S, Khurana N, Lal P. Does tumor stroma ratio of breast cancer trucut biopsy determine response to neoadjuvant therapy?. Indian J Pathol Microbiol 2020;63, Suppl S1:113-6
|How to cite this URL:|
Mallya V, Singh V, Kaur N, Yadav P, Mandal S, Khurana N, Lal P. Does tumor stroma ratio of breast cancer trucut biopsy determine response to neoadjuvant therapy?. Indian J Pathol Microbiol [serial online] 2020 [cited 2022 Jan 25];63, Suppl S1:113-6. Available from: https://www.ijpmonline.org/text.asp?2020/63/5/113/279546
| Introduction|| |
Breast Cancer is major cause of morbidity and mortality in India with age adjusted rates of 25.8 and a mortality of 12.7 per 100,000. Neoadjuvant therapy (NAT) is administered before the initiation of loco-regional treatment. Also termed as primary systemic therapy or preoperative therapy, the size of the primary tumor, the presence or absence of locally advanced disease and the hormone receptor status are the key factors in determining the relevance of NAT. Breast cancer is a heterogenous disease, it is imperative to look for response to treatment as it determines the disease free and overall survival. This is done by measuring the residual cancer burden (RCB) in post therapy specimens, which categorizes patients into responders (RCB 0- pathological complete response and RCB I-minimal residual disease) and nonresponders (RCB II –moderate residual disease and RCB III- extensive residual disease). The RCB is a numerical value generated by a webpage-based calculator from the MD Anderson Cancer Center, Houston, Texas, United States of America (www3.mdanderson.org/app/medcalc/index.cfm?pagename = jsconvert3).
This incorporates five parameters of prognostic significance, namely, the size of primary tumor bed, the percentage cellularity, the percentage of in situ component, presence or absence of nodal metastasis and the diameter of the largest node.
The cross talk between the tumor and its stroma has gained momentum in recent times. The Tumor Microenvironment (TME) comprises of fibroblasts, cells of the immune system, blood vessels, adipose tissue, and products produced by these cells that enhance and promote their growth and spread. The Cancer associated Fibroblasts (CAF) are found at the invasive margins of tumor and produce tumor promoting growth factors, cytokines, and extracellular matrix (ECM) remodeling enzymes that play a role in dissemination of cancer by inducing vascular and cancer cell proliferation.,,, The cells of the immune system produce angiogenic factors accumulating in the hypoxic and necrotic areas around the tumor, thus enhancing tumor activity. The angiogenic factors produce vessels that are leaky promoting tumor progression. The fat cells produce adipokines which help in the release of fatty acids that act as food for the tumor cells thus promoting growth [Figure 1].,,
The tumor stroma ratio (TSR) or the cancer percentage (CP) is the ratio of tumor cells to its microenvironment, emerging as a prognostic indicator, is known to influence overall and disease-free survival. It has known to be of prognostic significance in solid epithelial neoplasms such as breast, oral, esophagus, colon, and stomach.,, Here we aimed to evaluate the role of TSR in determining response to NAT in breast carcinoma patients by assessing its correlation with RCB. To the best of our knowledge this has not been investigated before.
| Materials and Methods|| |
A retrospective study was carried out on patients with Ductal Carcinoma breast (NOS) carcinoma who underwent trucut core biopsy before undergoing neodjuvant therapy (with chemotherapy/trastuzumab/both) followed by MRM between August 2016 to July 2018. Baseline characteristics studied were age, menopausal status, hormone receptor, and Her2neu status. TSR in core biopsies were studied independently by 2 pathologists (VM, VS) on Hemotoxylin and Eosin (H and E) stained slides. The maximum stroma rich area was assessed with a 10X objective. Extra caution was taken to select an area which had cells all around the circumferential border. Stromal percentage was scored in multiples of 10 and was defined as stroma poor (TSR >50%) [Figure 2]a or stroma rich (TSR <50%) [Figure 2]b. MRM with axillary dissection specimen of these patients were studied in a blinded manner by the same 2 pathologists. Pathological data recorded were size of primary tumor bed [Figure 3]a and [Figure 3]b (which was assessed from the detailed archives storing the gross description and further confirmed on microscopy), percentage cellularity [Figure 4], percentage of in situ component [Figure 5], nodal status and diameter of largest node. Residual cancer burden (RCB) was calculated using the MD Anderson RCB calculator which takes into consideration the aforementioned pathological characteristics and quantifies responsiveness to neo-adjuvant therapy. A numerical RCB value is further stratified into RCB classes 0, 1, 2 and 3 which indicate complete, good, partial and no response, respectively. Classes 0 and 1 are considered as responders and classes 3 and 4 as non-responders. Statistical analysis was done to assess the correlation of TSR to the final RCB, using SPSS version 20.
|Figure 2: (a) Photomicrograph showing a stroma rich core biopsy. (H and E, ×100). (b) Photomicrograph showing a stroma poor core biopsy (H and E, ×100)|
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|Figure 3: (a) Photomicrograph of tumor bed showing foamy histiocytes, calcification, tumor associated lymphoid proliferation, calcification, and tumor cells unresponsive to therapy. (H and E, ×100). (b) Photomicrograph of tumor bed showing fibrosis, blood vessels, and hemosiderin laden macrophages. (H and E, ×100)|
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|Figure 4: Photomicrograph showing the residual cellularity separated by dense fibrosis unresponsive to therapy (H and E, ×400)|
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|Figure 5: Photomicrograph showing the in situ component of tumor resistant to NAT (H and E, ×100)|
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| Results|| |
A total of 62 patients with complete data were analyzed. Only female patients were considered for study with mean (SD) age at the time of diagnosis being 48 (11) years. Twenty-seven (43%) patients were post-menopausal. Triple positivity, triple negativity, and non-triple negativity was seen in 5 (8%), 19 (31%), and 38 (61%) patients, respectively. Twenty-eight (45%) and 34 (55%) patients had stroma rich and stroma poor core biopsies respectively. Final pathological findings were as follows; Median (IQR) of: Tumor bed area was 1500 (2400) sq mm and percentage cellularity was 11% (10). Twenty-nine (47%) patients were node positive. Median (IQR) node number was 2 (4) and nodal diameter was 11.3 mm (10). Mean (SD) RCB was 2.12 (1.53). RCB class 0, 1, 2 and 3 was seen in 12 (19.3%), 14 (22.5%),15 (24.1%) and 21 (33.8%) patients, respectively. Totally, 26 (42%) patients were considered to be responders and 36 (58%) non-responders. Among stroma rich patients, only 3 (10%) were responders and 25 (90%) non-responders to NAT and among stroma poor patients 23 (68%) responded well and 11 (32%) did not [Table 1] and [Table 2]. On univariate analysis, only TSR correlated negatively with RCB (60% correlation, P < 0.001), none of the other factors determined RCB.
| Discussion|| |
Malignant epithelial tumors consist of two components, tumor cells, and surrounding supporting framework that is the stroma. The stromal component has been an area of extensive research, as it plays a role in tumor motility, growth, invasiveness, angiogenesis, and metastasis. TSR is a surrogate marker contributing to tumor aggressiveness as it is shown that low stroma tumors (TSR >50%) were associated with a better prognosis and increased disease free survival and high stroma tumors (TSR <50%) with an adverse outcome.,,,
The role of TSR in the prognosis of epithelial malignancies has been investigated. Wang K et al. stated that stroma rich esophageal squamous cell carcinomas were associated with a poor outcome. West NP et al. also postulated that low TSR was associated with poor cancer specific survival in colorectal carcinoma. Stroma rich oral cancers had a poor outcome and could be used for prognostication as concluded by Almangush A et al. Similarly in breast carcinoma, the amount of tumor associated stroma was found to be an independent risk factor for disease free overall survival.,
The unpredictable response to NAT is explained by the heterogeneity of the disease., It is extremely important to evaluate the response to NAT as it helps to identify residual disease (RD) and predict distant relapse free survival. The Residual Cancer Burden (RCB) calculator incorporates routinely evaluated variables on the post treatment Modified Radical Mastectomy Specimens. These include primary tumor bed dimensions that were that were recorded after gross assessment of the sample and further confirmed on reviewing the microscopy, the percentage cellularity of the tumor bed, the percentage of in situ component, the nodal status and the diameter of the largest positive node. The RCB is tabulated as RCB I- pathological complete response, RCB II-minimal RD, RCB III- moderate RD, RCB III- extensive RD. RCB 0 and I were responders and RCB II and III non responders. Here in the present study we tried to analyze the significance of TSR on trucut biopsies in determining the response to NAT by evaluating RCB on post treatment surgical resections of the corresponding patients. It was inferred that a high tumor stroma ratio resulted in low residual disease and a low TSR was associated with a higher RCB class. The therapeutic drugs act on rapidly proliferating cells, hence more the cancer percentage, the better the response of tumor to the drugs., None of the other parameters studied (age, menopausal status, hormonal or Her2/neu status) correlated with the RCB. Hence TSR is a simple method, requiring no additional cost and can be incorporated in daily practice. This could potentially help oncologists to decide about further management at the level of breast cancer trucut biopsy itself and could be a potential tool to predict responsiveness to neo-adjuvant therapy. Larger studies are required to validate the association of TSR with RCB.
There is a bidirectional talk between the stroma and tumor cells. The stroma primarily composed of the adaptive and innate immune cells, fibroblasts, vascular, and lymphatic endothelial cells and the adipocytes, plays a role in tumor growth, progression, and epithelial mesenchymal transition. A stroma rich tumor has more of these constituents and less of tumor cells. The fibroblasts are either in a normal state or activated state. In breast cancers especially the stromas rich breast cancers 80% fibroblasts are in the activated state. Drugs inhibiting activation of fibroblasts and antifibrotic agents may reduces aggressiveness of the lesions. These may also reduce the collagen content of the microenvironment thus allowing better drug delivery. Antiangiogenic drugs can also be potential oncotargets in future. Administering anti TGF-β therapy could prevent development of osteolytic metastasis. Targeting the tumor microenvironment is a new concept in anti-cancer therapeutics still in trial phases and will require larger studies over longer periods to see its efficacy.,
| Conclusion|| |
Tumor stroma ratio is a simple, useful, and practical parameter to predict residual cancer burden following neo-adjuvant therapy in breast cancer. It is worth studying this in multicentric studies with larger number of patients.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Malvia S, Bagadi SA, Dubey US, Saxena S. Epidemiology of breast cancer in Indian women. Asia Pacific J Clin Oncol 2017;13:289-95.
Teshome M, Hunt KK. Neoadjuvant therapy in the treatment of breast cancer. Surg Oncol Clin N
Symmans WF, Peintinger F, Hatzis C, Rajan R, Kuerer H, Valero V, et al
. Measurement of residual breast cancer burden to predict survival after neoadjuvant chemotherapy. J Clin Oncol 2007;25:4414-22.
Park JH, Richards CH, McMillan DC, Horgan PG, Roxburgh CSD. The relationship between tumour stroma percentage, the tumour microenvironment and survival in patients with primary operable colorectal cancer. Annals of Oncology 2014;25:644-51.
Roeke T, Sobral LM, Dekker TJA, Wesseling J, Vincent THBMS, Rob AEMT, et al
. The prognostic value of the tumour –stroma ratio in primary operable invasive cancer of the breast: A validation study. Breast Cancer Res Treat 2017;166:435-45.
Moorman AM, Vink R, Heijmans HJ, Palen J, Kouwenhoven EA. The prognostic value of tumour stroma ratio in triple negative breast cancer. Eur J Surg Oncol 2012;38:307-13.
De Kruijf EM, Van Nes JG, Van de Velde CJ. Tumour stroma ratio in primary tumour is a prognostic factor in early breast cancer patients, especially in triple negative carcinoma patients. Breast Cancer Res Treat 2011;125:687-96.
Chen F, Zhuang X, Lin L, Yu P, Wang Y, Shi Y, et al
. New horizons in tumor microenvironment biology: Challenges and opportunities. BMC Med 2015;13:45.
Kalluri R, Zeisberg M. Fibroblasts in cancer. Nat Rev Cancer 2006;6:392-401.
Ohlund D, Elyada E, Tuveson D. Fibroblast heterogeneity in the cancer wound. J Exp Med 2014;211:1503-23.
Wang M, Zhao J, Zhang L, Wei F, Lian Y, Wu Y, et al.
Role of tumor microenvironment in tumorigenesis. J Cancer 2017;8:761-73.
Wang K, Ma W, Wang J, Yu L, Zhang X, Wang Z, et al
. Tumour stroma ratio is an independent predictor for survival in esophageal squamous cell carcinoma. J Thorac Oncol 2012;7:1457-61.
West NP, Dattani M, McShane P, Hutchins G, Grabsch J, Mueller W, et al
. The proportion of tumour cells is an independent predictor for survival in colorectal cancer patients. Br J Cancer 2010;102:1519-23.
Almangush A, Heikkinen I, Bakhti N, Makinen LK, Kauppila JH, Pukkila M, et al
. Prognostic impact of tumour stroma ratio in early-stage oral tongue cancers. Histopathology 2018;72:1128-35.
Vangangelt KMH, Van PGW, Engels CC, Putter H, Liefers GJ, Smit VTHBM, et al
. Prognostic value of tumor–stroma ratio combined with the immune status of tumors in invasive breast carcinoma. Breast Cancer Res Treat 2018;168:601-12.
Rubovszky G, Horváth Z. Recent advances in the neoadjuvant treatment of breast cancer. J Breast Cancer 2017;20:119-31.
Dykes SS, Hughes VS, Wiggins JM, Fasanya HO, Tanaka M, Siemann D. Stromal cells in breast cancer as a potential therapeutic target. Oncotarget 2018;9:23761-79.
Cid S, Eiro N, Fernández B, Sánchez R, Andicoechea A, Fernández-Muñiz PI, et al
. Prognostic Influence of Tumor Stroma on Breast Cancer Subtypes. Clin Breast Cancer 2017;18:e123-33.
Department of Pathology, Maulana Azad Medical College, New Delhi - 110 002
Source of Support: None, Conflict of Interest: None
[Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5]
[Table 1], [Table 2]
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