If we want to understand the characteristics of a country or a city, we pay attention to the composition of the landscape and the environment. In geography, it is quite obvious that the spatial organization of houses, forests, streets, and meadows is central for the characterization of the environmental conditions and the development of a city or a country. It is becoming increasingly evident that we need to use a similar spatial approach to evaluate tumor heterogeneity. To understand tissue function, the spatial relationship of different cell types is important. Spatial relationships offer options for interactions of neighboring cells and also provide constraints that limit the interaction of cell types that are separated within a tumor. In tumor biology, we therefore need to do more than just measure the expression of a marker, the rate of a mutation, or the numbers of immune cells. The paper by Heindl etal. (1) published in this issue of the Journal gives an elegant example of how the quantitative analysis of spatial relationships between tumor and immune cells can provide insights into new parameters of tumor progression. In a large cohort of 1178 hormone receptor–positive breast cancers from the ATAC study, different cell types were automatically identified in scanned hematoxylin and eosin (H&E) images. With a spatial data clustering method originally developed for the analysis of geographic data by Getis and Ord (2), the authors defined so-called cancer or immune hotspots, in which the density of cancer cells or lymphocytes is statistically significantly higher than the average density in the tumor. Both parameters can be combined, resulting in so-called immune cancer hotspots, indicating close interactions between immune cells and cancer cells. As a main result, the authors found that the total abundance of immune cells—without their spatial relationship—was not relevant for prognosis. In contrast, when they included spatial organization patterns, the presence of immune cell hotspots was associated with reduced survival in the ATAC cohort of hormone receptor–positive tumors. This finding is particularly interesting because the role of immune infiltrates in hormone receptor–positive breast cancer is not completely clear as most studies have so far focused on the triple-negative and human epidermal growth factor receptor 2 (HER2)–positive tumor subtype. In triple-negative and HER2-positive tumors, an increased number of tumor-infiltrating lymphocytes is related to increased chemotherapy response and improved survival (3,4,5,6,7). Interestingly, Yuan and colleagues have already tested their spatial clustering approach in a cohort of triple-negative tumors in a previous project (8). They were able to show that increased immune cell clusters (and increased overall TIL rates) are a positive prognostic factor in triple-negative breast cancer, validating the results of previous studies on the positive role of immune markers in these tumor types. This suggests that the negative prognostic effect in luminal breast cancer is not due to the different method used, but that this might be a biological difference between luminal and triple-negative tumors. In luminal tumors, disease recurrence and survival are driven by the existence of disseminated tumor cells and micrometastases that remain clinically undetected for a long time. It is intriguing to speculate that immune evasion strategies are relevant for the survival of these dormant tumor cells. The interaction of immune cells with different subclones within a tumor might determine the memory function of the immune system that controls elimination of disseminated tumor cell clones. The heterogeneity of tumor-immune interactions could then be an indicator of the emergence of tumor subclones with successful immune escape mechanisms. This model would partly explain the findings reported by Heindl etal. (1), but additional studies are required to support and validate this hypothesis. The spatial heterogeneity of the tumor–immune cell landscape might be more relevant in slow-growing tumors—such as luminal breast cancer—where the low growth rate could increase the molecular differences between different areas of the tumor. In contrast, in a rapidly growing tumor, the molecular diversity could be increased even in cells that are spatially close to each other. In both scenarios, the tumor would be heterogeneous on a molecular level, but only in the slow-growing tumor would the heterogeneity be linked to spatial organization patterns. There are still relevant challenges and open questions for further translation of this finding into new diagnostic and therapeutic concepts. The use of H&E-stained slides does not permit the identification of different immune cell subtypes. On the other hand, recent molecular studies have used gene expression algorithms that separate different types of immune cells in transcriptomic data sets without a spatial resolution (9,10,11). For the future, it would be interesting to combine both approaches and use molecular quantitative in situ methods that allow marker quantification and assessment of spatial differences in parallel. The current diagnostic guidelines of the International TIL working group (12) are made for visual evaluation by pathologists, and their robustness has been shown in international ring trials (13). The agreement between the standardized TIL evaluation and the image analysis–based segmentation in the ATAC cohort is currently very limited. For further improvement of diagnostic concepts, it would therefore be interesting to generate standardized diagnostic guidelines for spatial evaluation of immune cells in tumors. Relevant challenges include the variability of different H&E stainings, the robustness of the tissue segmentation algorithms, and the development and validation of cut-points for heterogeneity parameters across different cohorts. Furthermore, improved software packages would have to be designed to serve as clinically useful standardized digital diagnostics systems that could be used decentrally by pathologists in a routine diagnostic setting. The relevance of immunological parameters for chemotherapy response (14), in particular in triple-negative breast cancer, has been the basis for the first phase I and phase II trials investigating immune checkpoint inhibitors (15,16). At the annual American Society of Clinical Oncology meeting in 2017, a first neoadjuvant trial (17) of a combination of immune checkpoint inhibitors and chemotherapy reported increased pathological complete response rates in triple-negative tumors, but also in luminal breast cancer. For a better description of the immuno-biology of these tumors, a combination of quantitative molecular markers with spatial morphological parameters would be of great interest. Funding German Cancer Aid, TransLuminal-B project. Notes The funder had no role in the writing of the editorial or the decision to submit the editorial for publication. The authors have no conflicts of interest to disclose. Reference 1 Heindl A,, Sestak I,, Naidoo K,, Cuzick J,, Dowsett M,, Yuan Y. Relevance of spatial heterogeneity of immune infiltration for prediction risk of recurrence after endocrine therapy of ER+ breast cancer. J Natl Cancer Inst . 2018; 110( 2):djx137. 2 Getis A, Ord JK. The analysis of spatial association by use of distance statistics. Geograph Anal . 1992; 24( 3): 189– 206. Google Scholar CrossRef Search ADS 3 Denkert C, Loibl S, Noske A, et al. Tumor-associated lymphocytes as an independent predictor of response to neoadjuvant chemotherapy in breast cancer. J Clin Oncol . 2010; 28( 1): 105– 113. Google Scholar CrossRef Search ADS PubMed 4 Denkert C, von Minckwitz G, Brase JC, et al. Tumor-infiltrating lymphocytes and response to neoadjuvant chemotherapy with or without carboplatin in human epidermal growth factor receptor 2-positive and triple-negative primary breast cancers. J Clin Oncol. 2015; 33( 9): 983– 991. Google Scholar CrossRef Search ADS PubMed 5 Loi S, Sirtaine N, Piette F, et al. Prognostic and predictive value of tumor-infiltrating lymphocytes in a phase III randomized adjuvant breast cancer trial in node-positive breast cancer comparing the addition of docetaxel to doxorubicin with doxorubicin-based chemotherapy: BIG 02-98. J Clin Oncol . 2013; 31( 7): 860– 867. Google Scholar CrossRef Search ADS PubMed 6 Adams S, Gray RJ, Demaria S, et al. Prognostic value of tumor-infiltrating lymphocytes in triple-negative breast cancers from two phase III randomized adjuvant breast cancer trials: ECOG 2197 and ECOG 1199. J Clin Oncol . 2014; 32( 27): 2959– 2966. Google Scholar CrossRef Search ADS PubMed 7 Luen SJ, Salgado R, Fox S, et al. Tumour-infiltrating lymphocytes in advanced HER2-positive breast cancer treated with pertuzumab or placebo in addition to trastuzumab and docetaxel: A retrospective analysis of the CLEOPATRA study. Lancet Oncol. 2017; 18( 1): 52– 62. Google Scholar CrossRef Search ADS PubMed 8 Nawaz S, Heindl A, Koelble K, Yuan Y. Beyond immune density: Critical role of spatial heterogeneity in estrogen receptor-negative breast cancer. Mod Pathol. 2015; 28( 6): 766– 777. Google Scholar CrossRef Search ADS PubMed 9 Newman AM, Liu CL, Green MR, et al. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods. 2015; 12: 453– 457. Google Scholar CrossRef Search ADS PubMed 10 Gentles AJ, Newman AM, Liu CL, et al. The prognostic landscape of genes and infiltrating immune cells across human cancers. Nat Med. 2015; 21: 938– 945. Google Scholar CrossRef Search ADS PubMed 11 Bense RD, Sotiriou C, Piccart-Gebhart MJ, et al. Relevance of tumor-infiltrating immune cell composition and functionality for disease outcome in breast cancer. J Natl Cancer Inst . 2017; 109( 1):djw192. 12 Salgado R, Denkert C, Demaria S, et al. The evaluation of tumor-infiltrating lymphocytes (TILs) in breast cancer: Recommendations by an International TILs Working Group 2014. Ann Oncol. 2015; 26: 259– 271. Google Scholar CrossRef Search ADS PubMed 13 Denkert C, Wienert S, Poterie A, et al. Standardized evaluation of tumor-infiltrating lymphocytes in breast cancer: Results of the ring studies of the international immuno-oncology biomarker working group. Mod Pathol. 2016; 29( 10): 1155– 1164. Google Scholar CrossRef Search ADS PubMed 14 Zitvogel L, Kepp O, Kroemer G. Immune parameters affecting the efficacy of chemotherapeutic regimens. Nat Rev Clin Oncol. 2011; 8( 3): 151– 160. Google Scholar CrossRef Search ADS PubMed 15 Nanda R, Chow LQ, Dees EC, et al. Pembrolizumab in patients with advanced triple-negative breast cancer: Phase Ib KEYNOTE-012 Study. J Clin Oncol. 2016; 34( 21): 2460– 2467. Google Scholar CrossRef Search ADS PubMed 16 Adams S,, Diamond JR,, Hamilton EP, et al. Phase Ib trial of atezolizumab in combination with nab-paclitaxel in patients with metastatic triple-negative breast cancer (mTNBC). J Clin Oncol . 2016; 34(suppl; abstr 1009). 17 Nanda R, Liu MC, Yau C, et al. Pembrolizumab plus standard neoadjuvant therapy for high-risk breast cancer (BC): Results from I-SPY 2. 2017 ASCO Annual Meeting. J Clin Oncol . 2017; 35(suppl; abstr 506). © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: firstname.lastname@example.org.
JNCI: Journal of the National Cancer Institute – Oxford University Press
Published: Feb 1, 2018
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