Tumor infiltrating lymphocyte stratification of prognostic staging of early-stage triple negative breast cancer

The importance of integrating biomarkers into the TNM staging has been emphasized in the 8 th Edition of the American Joint Committee on Cancer (AJCC) Staging system. In a pooled analysis of 2148 TNBC-patients in the adjuvant setting, TILs are found to strongly up and downstage traditional pathological-staging in the Pathological and Clinical Prognostic Stage Groups from the AJJC 8 th edition Cancer Staging System. This suggest that clinical and research studies on TNBC should take TILs into account in addition to stage, as for example patients with stage II TNBC and high TILs have a better outcome than patients with stage I and low TILs.Peer reviewe

Tumor-Infiltrating Lymphocytes and Prognosis: A Pooled Individual Patient Analysis of Early-Stage Triple-Negative Breast Cancers

PURPOSE: The aim of the current study was to conduct a pooled analysis of studies that have investigated the prognostic value of tumor-infiltrating lymphocytes (TILs) in early-stage triple negative breast cancer (TNBC). METHODS: Participating studies had evaluated the percentage infiltration of stromally located TILs (sTILs) that were quantified in the same manner in patient diagnostic samples of early-stage TNBC treated with anthracycline-based chemotherapy with or without taxanes. Cox proportional hazards regression models stratified by trial were used for invasive disease-free survival (iDFS; primary end point), distant disease-free survival (D-DFS), and overall survival (OS), fitting sTILs as a continuous variable adjusted for clinicopathologic factors. RESULTS: We collected individual data from 2,148 patients from nine studies. Average age was 50 years (range, 22 to 85 years), and 33% of patients were node negative. The average value of sTILs was 23% (standard deviation, 20%), and 77% of patients had 1% or more sTILs. sTILs were significantly lower with older age ( P = .001), larger tumor size ( P = .01), more nodal involvement ( P = .02), and lower histologic grade ( P = .001). A total of 736 iDFS and 548 D-DFS events and 533 deaths were observed. In the multivariable model, sTILs added significant independent prognostic information for all end points (likelihood ratio \u3c72, 48.9 iDFS; P < .001; \u3c72, 55.8 D-DFS; P < .001; \u3c72, 48.5 OS; P < .001). Each 10% increment in sTILs corresponded to an iDFS hazard ratio of 0.87 (95% CI, 0.83 to 0.91) for iDFS, 0.83 (95% CI, 0.79 to 0.88) for D-DFS, and 0.84 (95% CI, 0.79 to 0.89) for OS. In node-negative patients with sTILs 65 30%, 3-year iDFS was 92% (95% CI, 89% to 98%), D-DFS was 97% (95% CI, 95% to 99%), and OS was 99% (95% CI, 97% to 100%). CONCLUSION: This pooled data analysis confirms the strong prognostic role of sTILs in early-stage TNBC and excellent survival of patients with high sTILs after adjuvant chemotherapy and supports the integration of sTILs in a clinicopathologic prognostic model for patients with TNBC. This model can be found at www.tilsinbreastcancer.org

Intra-tumor genetic heterogeneity and alternative driver genetic alterations in breast cancers with heterogeneous HER2 gene amplification

Background HER2 is overexpressed and amplified in approximately 15% of invasive breast cancers, and is the molecular target and predictive marker of response to anti-HER2 agents. In a subset of these cases, heterogeneous distribution of HER2 gene amplification can be found, which creates clinically challenging scenarios. Currently, breast cancers with HER2 amplification/overexpression in just over 10% of cancer cells are considered HER2-positive for clinical purposes; however, it is unclear as to whether the HER2-negative components of such tumors would be driven by distinct genetic alterations. Here we sought to characterize the pathologic and genetic features of the HER2-positive and HER2-negative components of breast cancers with heterogeneous HER2 gene amplification and to define the repertoire of potential driver genetic alterations in the HER2-negative components of these cases.Results We separately analyzed the HER2-negative and HER2-positive components of 12 HER2 heterogeneous breast cancers using gene copy number profiling and massively parallel sequencing, and identified potential driver genetic alterations restricted to the HER2-negative cells in each case. In vitro experiments provided functional evidence to suggest that BRF2 and DSN1 overexpression/amplification, and the HER2 I767M mutation may be alterations that compensate for the lack of HER2 amplification in the HER2-negative components of HER2 heterogeneous breast cancers.Conclusions Our results indicate that even driver genetic alterations, such as HER2 gene amplification, can be heterogeneously distributed within a cancer, and that the HER2-negative components are likely driven by genetic alterations not present in the HER2-positive components, including BRF2 and DSN1 amplification and HER2 somatic mutations

Pitfalls in assessing stromal tumor infiltrating lymphocytes (sTILs) in breast cancer

Abstract: Stromal tumor-infiltrating lymphocytes (sTILs) are important prognostic and predictive biomarkers in triple-negative (TNBC) and HER2-positive breast cancer. Incorporating sTILs into clinical practice necessitates reproducible assessment. Previously developed standardized scoring guidelines have been widely embraced by the clinical and research communities. We evaluated sources of variability in sTIL assessment by pathologists in three previous sTIL ring studies. We identify common challenges and evaluate impact of discrepancies on outcome estimates in early TNBC using a newly-developed prognostic tool. Discordant sTIL assessment is driven by heterogeneity in lymphocyte distribution. Additional factors include: technical slide-related issues; scoring outside the tumor boundary; tumors with minimal assessable stroma; including lymphocytes associated with other structures; and including other inflammatory cells. Small variations in sTIL assessment modestly alter risk estimation in early TNBC but have the potential to affect treatment selection if cutpoints are employed. Scoring and averaging multiple areas, as well as use of reference images, improve consistency of sTIL evaluation. Moreover, to assist in avoiding the pitfalls identified in this analysis, we developed an educational resource available at www.tilsinbreastcancer.org/pitfalls

Report on computational assessment of Tumor Infiltrating Lymphocytes from the International Immuno-Oncology Biomarker Working Group

Funder: U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)Funder: National Center for Research Resources under award number 1 C06 RR12463-01, VA Merit Review Award IBX004121A from the United States Department of Veterans Affairs Biomedical Laboratory Research and Development Service, the DOD Prostate Cancer Idea Development Award (W81XWH-15-1-0558), the DOD Lung Cancer Investigator-Initiated Translational Research Award (W81XWH-18-1-0440), the DOD Peer Reviewed Cancer Research Program (W81XWH-16-1-0329), the Ohio Third Frontier Technology Validation Fund, the Wallace H. Coulter Foundation Program in the Department of Biomedical Engineering and the Clinical and Translational Science Award Program (CTSA) at Case Western Reserve University.Funder: Susan G Komen Foundation (CCR CCR18547966) and a Young Investigator Grant from the Breast Cancer Alliance.Funder: The Canadian Cancer SocietyFunder: Breast Cancer Research Foundation (BCRF), Grant No. 17-194Abstract: Assessment of tumor-infiltrating lymphocytes (TILs) is increasingly recognized as an integral part of the prognostic workflow in triple-negative (TNBC) and HER2-positive breast cancer, as well as many other solid tumors. This recognition has come about thanks to standardized visual reporting guidelines, which helped to reduce inter-reader variability. Now, there are ripe opportunities to employ computational methods that extract spatio-morphologic predictive features, enabling computer-aided diagnostics. We detail the benefits of computational TILs assessment, the readiness of TILs scoring for computational assessment, and outline considerations for overcoming key barriers to clinical translation in this arena. Specifically, we discuss: 1. ensuring computational workflows closely capture visual guidelines and standards; 2. challenges and thoughts standards for assessment of algorithms including training, preanalytical, analytical, and clinical validation; 3. perspectives on how to realize the potential of machine learning models and to overcome the perceptual and practical limits of visual scoring

Application of a risk-management framework for integration of stromal tumor-infiltrating lymphocytes in clinical trials

Funder: Breast Cancer Research Foundation (BCRF); doi: https://doi.org/10.13039/100001006Abstract: Stromal tumor-infiltrating lymphocytes (sTILs) are a potential predictive biomarker for immunotherapy response in metastatic triple-negative breast cancer (TNBC). To incorporate sTILs into clinical trials and diagnostics, reliable assessment is essential. In this review, we propose a new concept, namely the implementation of a risk-management framework that enables the use of sTILs as a stratification factor in clinical trials. We present the design of a biomarker risk-mitigation workflow that can be applied to any biomarker incorporation in clinical trials. We demonstrate the implementation of this concept using sTILs as an integral biomarker in a single-center phase II immunotherapy trial for metastatic TNBC (TONIC trial, NCT02499367), using this workflow to mitigate risks of suboptimal inclusion of sTILs in this specific trial. In this review, we demonstrate that a web-based scoring platform can mitigate potential risk factors when including sTILs in clinical trials, and we argue that this framework can be applied for any future biomarker-driven clinical trial setting

mRNA splicing deregulation during metastatic progression of breast cancers

Le contrôle post-transcriptionnel de l'expression des gènes représente un vaste ensemble de processus biologiques autour de la machinerie des ARNm, jouant un rôle majeur dans la création de la diversité du répertoire protéique. La dérégulation de ces processus, générant un phénotype altéré, pourrait contribuer à la progression tumorale dans les cancers du sein. Nos travaux se sont axés sur l'étude de l'épissage alternatif des pré-ARNm et la dérégulation de la machinerie de l'épissage dans la progression métastatique des cancers du sein. Dans un modèle murin de carcinome mammaire, nous avons identifié des variants d'épissage spécifiquement associés au potentiel métastatique. Dans une large cohorte de patientes, nous avons montré que l'expression de certains de ces variants dans des tumeurs est associée à un pronostic défavorable. Enfin, nous avons caractérisé le profil d'expression des protéines régulatrices de l'épissage dans plusieurs séries de cancer du sein. Cette étude offre de nouvelles connaissances et perspectives pour le développement de biomarqueurs de la progression tumorale.Alternative RNA processing is a mechanism that plays a critical role for creation of protein diversity through selective inclusion or exclusion of RNA sequences during post-transcriptional control of gene expression. We hypothesized that alteration in this process might contribute greatly to tumour development and progression in breast cancer. The aim of our study was to identify and characterize defects in alternative splicing during breast tumour progression. In a murine model, we could identify specific mRNA splicing variants associated with metastatic development. In a large cohort of breast cancer patients, expression of a subset of these variants was correlated to poor prognosis. Finally, we characterised the expression profile of a large panel of proteins of the splicing machinery in breast cancer. Our study provides new insights in the understanding of mechanisms leading to tumour progression and perspectives for the development of new biomarkers and therapies

Dérégulation de l'épissage des pré-ARNm dans la progression métastatique des cancers du sein

Le contrôle post-transcriptionnel de l'expression des gènes représente un vaste ensemble de processus biologiques autour de la machinerie des ARNm, jouant un rôle majeur dans la création de la diversité du répertoire protéique. La dérégulation de ces processus, générant un phénotype altéré, pourrait contribuer à la progression tumorale dans les cancers du sein. Nos travaux se sont axés sur l'étude de l'épissage alternatif des pré-ARNm et la dérégulation de la machinerie de l'épissage dans la progression métastatique des cancers du sein. Dans un modèle murin de carcinome mammaire, nous avons identifié des variants d'épissage spécifiquement associés au potentiel métastatique. Dans une large cohorte de patientes, nous avons montré que l'expression de certains de ces variants dans des tumeurs est associée à un pronostic défavorable. Enfin, nous avons caractérisé le profil d'expression des protéines régulatrices de l'épissage dans plusieurs séries de cancer du sein. Cette étude offre de nouvelles connaissances et perspectives pour le développement de biomarqueurs de la progression tumorale.Alternative RNA processing is a mechanism that plays a critical role for creation of protein diversity through selective inclusion or exclusion of RNA sequences during post-transcriptional control of gene expression. We hypothesized that alteration in this process might contribute greatly to tumour development and progression in breast cancer. The aim of our study was to identify and characterize defects in alternative splicing during breast tumour progression. In a murine model, we could identify specific mRNA splicing variants associated with metastatic development. In a large cohort of breast cancer patients, expression of a subset of these variants was correlated to poor prognosis. Finally, we characterised the expression profile of a large panel of proteins of the splicing machinery in breast cancer. Our study provides new insights in the understanding of mechanisms leading to tumour progression and perspectives for the development of new biomarkers and therapies