Salgado, Roberto

Importance The association of tumor-infiltrating lymphocyte (TIL) abundance in breast cancer tissue with cancer recurrence and death in patients with early-stage triple-negative breast cancer (TNBC) who are not treated with adjuvant or neoadjuvant chemotherapy is unclear. Objective To study the association of TIL abundance in breast cancer tissue with survival among patients with early-stage TNBC who were treated with locoregional therapy but no chemotherapy. Design, Setting, and Participants Retrospective pooled analysis of individual patient-level data from 13 participating centers in North America (Rochester, Minnesota; Vancouver, British Columbia, Canada), Europe (Paris, Lyon, and Villejuif, France; Amsterdam and Rotterdam, the Netherlands; Milan, Padova, and Genova, Italy; Gothenburg, Sweden), and Asia (Tokyo, Japan; Seoul, Korea), including 1966 participants diagnosed with TNBC between 1979 and 2017 (with follow-up until September 27, 2021) who received treatment with surgery with or without radiotherapy but no adjuvant or neoadjuvant chemotherapy. Exposure TIL abundance in breast tissue from resected primary tumors. Main Outcomes and Measures The primary outcome was invasive disease-free survival [iDFS]. Secondary outcomes were recurrence-free survival [RFS], survival free of distant recurrence [distant RFS, DRFS], and overall survival. Associations were assessed using a multivariable Cox model stratified by participating center. Results This study included 1966 patients with TNBC (median age, 56 years [IQR, 39-71]; 55% had stage I TNBC). The median TIL level was 15% (IQR, 5%-40%). Four-hundred seventeen (21%) had a TIL level of 50% or more (median age, 41 years [IQR, 36-63]), and 1300 (66%) had a TIL level of less than 30% (median age, 59 years [IQR, 41-72]). Five-year DRFS for stage I TNBC was 94% (95% CI, 91%-96%) for patients with a TIL level of 50% or more, compared with 78% (95% CI, 75%-80%) for those with a TIL level of less than 30%; 5-year overall survival was 95% (95% CI, 92%-97%) for patients with a TIL level of 50% or more, compared with 82% (95% CI, 79%-84%) for those with a TIL level of less than 30%. At a median follow-up of 18 years, and after adjusting for age, tumor size, nodal status, histological grade, and receipt of radiotherapy, each 10% higher TIL increment was associated independently with improved iDFS (hazard ratio [HR], 0.92 [0.89-0.94]), RFS (HR, 0.90 [0.87-0.92]), DRFS (HR, 0.87 [0.84-0.90]), and overall survival (0.88 [0.85-0.91]) (likelihood ratio test, P < 10e-6). Conclusions and Relevance In patients with early-stage TNBC who did not undergo adjuvant or neoadjuvant chemotherapy, breast cancer tissue with a higher abundance of TIL levels was associated with significantly better survival. These results suggest that breast tissue TIL abundance is a prognostic factor for patients with early-stage TNBC.

Abstract Background: The prognostic value of stromal tumor-infiltrating lymphocytes (TILs) as a biomarker for triple-negative breast cancer (TNBC) has been extensively demonstrated in patients (pts) receiving (neo)adjuvant systemic therapy. In addition, several small studies suggest that a subset of pts with early-stage TNBC and high TILs have excellent long-term outcomes, even in the absence of systemic therapy [1-3]. However, data on the absolute risk of TNBC recurrence according to TIL levels in the absence of systemic therapy are limited and critical to inform the design of future systemic therapy de-escalation clinical trials. Methods: We conducted an individual patient data pooled analysis of 12 international cohorts of pts with TNBC treated with locoregional therapy but no systemic therapy. TNBC was defined as tumors with estrogen and progesterone receptor of < 1% and HER2 negative (IHC 0, 1+ or IHC 2+ and FISH negative) per local evaluation. TILs were locally assessed in hematoxylin & eosin-stained slides according to the International Immuno-Oncology Biomarker Working Group guidelines (www.tilsinbreastcancer.org). We used the Kaplan-Meier method to assess survival outcomes according to prespecified TIL thresholds: 30% and 50%. Confidence intervals (CI) for survival probabilities were calculated using a percentile bootstrap method. The primary endpoint was invasive disease-free survival (iDFS, STEEP 2.0 definition). Key secondary outcomes included recurrence-free survival (RFS), distant disease-free survival (DDFS) and overall survival (OS). Results: 1,835 pts diagnosed with TNBC between 1982 and 2017 who did not receive systemic therapy were included. The median age at diagnosis was 56 (IQR 38-71). Menopausal status was known in 1,184 women, of whom 78% were post-menopausal. The median tumor size was 2.0 cm (IQR 1.2-2.6). Most pts (87%) had no axillary lymph node involvement (N0). Most tumors were invasive ductal carcinoma (74%) and grade 3 (70%). The median level of TILs was 15% (IQR 5-40). The median duration of follow-up was 30.4 years (95% CI 29.9, 31.1). A total of 950 (52%) iDFS, 828 (45%) RFS, 767 (42%) DDFS events, and 604 (33%) deaths were observed. In multivariable analyses, higher TILs were independently associated with improved iDFS, RFS, DDFS, and OS beyond clinicopathological factors (likelihood ratio p< 10e-6). Each 10% increment in stromal TILs was associated with an 8% (95% CI: 6-11), 10% (95% CI: 7-13), and 13% (95% CI: 10-15) reduction in the risk of experiencing an iDFS, RFS or DDFS event, and with a 12% (95% CI: 9-15) reduction in the risk of death. iDFS, RFS, DDFS and OS rates according to different TIL thresholds and nodal status are shown in the Table. Of note, the RFS estimates (which exclude second non-breast primaries and contralateral breast cancers) were consistently higher than the iDFS counterparts (which include both), consistent with a high rate of contralateral breast cancers and second primary tumors in this cohort. Notably, patients with node-negative—and especially stage I—TNBC with high TILs had excellent survival rates at 10-year follow-up. Conclusion: TILs are highly prognostic in pts with systemically untreated early-stage TNBC. Pts with pN0 (and especially stage I) TNBC with high TILs exhibited very favorable long-term outcomes even in the absence of systemic therapy. These data define the natural history of TIL-rich TNBC pts and are crucial to identifying the optimal patient population for future chemotherapy and immunotherapy de-escalation clinical trials. References: [1] Leon-Ferre et al, 2017, PMID: 28913760 [2] Park et al, 2019, PMID: 31566659 [3] de Jong et al, 2022, PMID: 35353548 Table 5 and 10-year survival endpoints according TIL level, nodal status, and stage Citation Format: Roberto A. Leon-Ferre, Sarah Flora Jonas, Roberto Salgado, Sherene Loi, Vincent De Jong, Jodi M. Carter, Torsten Nielson, Samuel Leung, Nazia Riaz, Giuseppe Curigliano, Carmen Criscitiello, Vincent Cockenpot, Matteo Lambertini, Vera Suman, Barbro Linderholm, John WM Martens, Carolien HM van Deurzen, Mieke Timmermans, Tatsunori Shimoi, Shu Yazaki, Masayuki Yoshida, Sung-Bae Kim, Hee Jin Lee, Maria Vittoria Dieci, Guillaume Bataillon, Anne Salomon, Fabrice Andre, Marleen Kok, Sabine Linn, Matthew P. Goetz, Stefan Michiels. Stromal tumor-infiltrating lymphocytes identify early-stage triple-negative breast cancer patients with favorable outcomes at 10-year follow-up in the absence of systemic therapy: a pooled analysis of 1835 patients [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr PD9-05.

Abstract Background: Triple-negative breast cancers (TNBC) exhibit high rates of recurrence and mortality. However, recent studies suggest that a subset of patients (pts) with early-stage TNBC enriched in tumor-infiltrating lymphocytes (TILs) have excellent clinical outcomes even in the absence of systemic therapy. Additional histological biomarkers that could identify pts for future systemic therapy escalation/de-escalation strategies are of great interest. TNBC are frequently highly proliferative with abundant mitoses. However, classic markers of proliferation (manual mitosis counting and Ki-67) appear to offer no prognostic value. Here, we evaluated the prognostic effects of automated mitotic spindle hotspot (AMSH) counting on RFS in independent cohorts of systemically untreated early-stage TNBC. Methods: AMSH counting was conducted with a state-of-the-art deep learning algorithm trained on the detection of mitoses within 2 mm2 areas with the highest mitotic density (i.e. hotspots) in digital H&E images. Details of the development, training and validation of the algorithm were published previously [1] in a cohort of unselected TNBC. We obtained AMSH counts in a centrally confirmed TNBC cohort from Mayo Clinic [2] and focused our analysis on pts who received locoregional therapy but no systemic therapy. Using a fractional polynomial analysis with a multivariable proportional hazards regression model, we confirmed the assumption of linearity in the log hazard for the continuous variable AMSH and evaluated whether AMSH counts were prognostic of RFS. We corroborated our findings in an independent cohort of systemically untreated TNBC pts from the Radboud University Medical Center in the Netherlands (Radboud Cohort). Results are reported at a median follow-up of 8.1 and 6.7 years for the Mayo and Netherlands cohorts, respectively. Results: Among 182 pts with who did not receive systemic therapy in the Mayo Cohort, 140 (77%) with available AMSH counts were included. The mean age was 61 (range: 31-94), 71% were postmenopausal, 67% had tumors ≤ 2cm, and 83% were node-negative. As expected, most tumors were Nottingham grade 3 (84%) and had a high Ki-67 proliferation index (54% with Ki-67 >30%). Most tumors (73%) had stromal TILs ≤ 30%. The median AMSH count was 18 (IQR: 8, 42). AMSH counts were linearly associated with grade and tumor size, with the proportion of pts with grade 3 tumors and size > 2 cm increasing as the AMSH counts increased (p=0.007 and p=0.059, respectively). In a multivariate model controlling for nodal status, tumor size, and stromal TILs, AMSH counts were independently associated with RFS (p< 0.0001). For every 10-point increase in the AMSH count, we observed a 17% increase in the risk of experiencing an RFS event (HR 1.17, 95% CI 1.08-1.26). We corroborated our findings in the Radboud Cohort (n=126). The mean age was 68 (range: 40-96), and 81% were node-negative. While the median AMSH count was 36 (IQR: 16-63), higher than in the Mayo Cohort (p=0.004), the prognostic impact was similar, with a significant association between AMSH count and RFS (p=0.028) in a multivariate model corrected for nodal status, tumor size, and stromal TILs. For every 10-point increase in the AMSH count in the Netherlands cohort, we observed a 9% increase in the risk of experiencing an RFS event (HR 1.09, 95% CI 1.01-1.17). RFS rates according to AMSH counts for both cohorts are shown in the Table. Conclusions: AMSH counting is a new proliferation biomarker that provides prognostic value independent of nodal status, tumor size, and stromal TILs in systemically untreated early-stage TNBC. Plans are underway to evaluate AMSH counts in additional cohorts of systemically untreated TNBC, and in other disease settings such as prior to neoadjuvant systemic therapy. If validated, this biomarker should be prospectively evaluated as a potential selection biomarker in clinical trials of systemic therapy de-escalation. References: 1. PMID: 29994086 2. PMID: 28913760 Table RFS according to AMSH counts in the Mayo and Radboud Cohorts Citation Format: Roberto A. Leon-Ferre, Jodi M. Carter, David Zahrieh, Jason P. Sinnwell, Roberto Salgado, Vera Suman, David Hillman, Judy C. Boughey, Krishna R. Kalari, Fergus J. Couch, James N. Ingle, Maschenka Balkenkohl, Francesco Ciompi, Jeroen van der Laak, Matthew P. Goetz. Mitotic spindle hotspot counting using deep learning networks is highly associated with clinical outcomes in patients with early-stage triple-negative breast cancer who did not receive systemic therapy [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P2-11-34.

Abstract Background Laboratory medicine has reached the era where promises of artificial intelligence and machine learning (AI/ML) seem palpable. Currently, the primary responsibility for risk-benefit assessment in clinical practice resides with the medical director. Unfortunately, there is no tool or concept that enables diagnostic quality assessment for the various potential AI/ML applications. Specifically, we noted that an operational definition of laboratory diagnostic quality – for the specific purpose of assessing AI/ML improvements – is currently missing. Methods A session at the 3rd Strategic Conference of the European Federation of Laboratory Medicine in 2022 on “ AI in the Laboratory of the Future” prompted an expert roundtable discussion. Here we present a conceptual diagnostic quality framework for the specific purpose of assessing AI/ML implementations. Results The presented framework is termed diagnostic quality model (DQM) and distinguishes AI/ML improvements at the test, procedure, laboratory, or healthcare ecosystem level. The operational definition illustrates the nested relationship among these levels. The model can help to define relevant objectives for implementation and how levels come together to form coherent diagnostics. The affected levels are referred to as scope and we provide a rubric to quantify AI/ML improvements while complying with existing, mandated regulatory standards. We present 4 relevant clinical scenarios including multi-modal diagnostics and compare the model to existing quality management systems. Conclusions A diagnostic quality model is essential to navigate the complexities of clinical AI/ML implementations. The presented diagnostic quality framework can help to specify and communicate the key implications of AI/ML solutions in laboratory diagnostics.