Salgado, Roberto

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 The discovery of molecular subtypes has corroborated the heterogeneity of breast cancers. But in clinical routine, treatment selection relies on measuring the aggressiveness of the tumor via histopathology, which is routinely based on grading, a prognostic yet laborious, poorly reproducible, and subjective procedure defined more than 30 years ago, before molecular subtypes existed. In this paper, we present an interpretable deep learning-based computer-aided diagnosis method to automate mitotic count, one of the components of grading; the method complies with current guidelines and automates hot-spot finding on full digital whole-slide images of routinely prepared pathologic slides stained with hematoxylin and eosin. We use the computer model to query the current “one-size-fits-all” mitotic count procedure by investigating subtype-specific prognostic value of mitotic count using an external independent large-scale multicentric dataset of 1,709 breast cancers. We show that for human epidermal growth factor-2 (HER2) positive tumors, mitotic count was not prognostic. In hormone-receptor positive/HER2 negative cancers, an average mitotic count density different from clinically used cut offs predicted worse recurrence free survival in multivariable analysis, adjusted for known clinical prognosticators. We provided some first experimental evidence that the prognostic role of mitotic count should be reviewed.

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.

Abstract Background: Disease relapse is observed in a significant proportion of HER2-positive breast cancer (BC) patients with residual disease (RD) after neoadjuvant treatment, as well as in a subgroup of those achieving pathological complete response (pCR). As the host immune response plays a key role in modulating the activity of anti-HER2 agents, we investigated the association of T- and B-cell receptor (TCR and BCR) repertoires with pCR and event-free survival (EFS) in the NeoALTTO phase 3 trial. Methods: RNA sequencing (RNAseq) data from baseline tumor biopsies were available for 254 patients out of the 455 enrolled in the study. Among those, 166 did not achieve a pCR defined as ypT0/is. Matched RNAseq data from RD samples were available for 43 cases. TCR/BCR repertoires were extracted from RNAseq data using the MiXCR software. TCR and BCR read counts, number of clones, evenness, Shannon entropy, Gini index, length of the complementarity determining region 3, top and second top clone proportion were evaluated. Survival analysis was performed using univariate and multivariate (adjusted for tumor size, nodal status, grade, estrogen receptor [ER] status, age and treatment arm) Cox proportional hazard models, while logistic regressions were used for pCR. False discovery rate (FDR) was obtained using Benjamini & Hochberg method. Results: Baseline TCR top (odds ratio [OR]=0.63 [95% CI 0.46-0.87], FDR=0.021) and second top (OR=0.57 [0.41-0.79], FDR=0.004) clone proportion were significantly associated with a lower probability of achieving pCR in the multivariate analysis. BCR evenness (hazard ratio [HR]=1.5 [1.2-2], FDR=0.015) and Gini index (HR=0.66 [0.52-0.85], FDR=0.015) were significantly associated with EFS in the multivariate analysis. In residual disease, BCR evenness and Gini index showed a similar trend in the EFS univariate analysis, while TCR read counts, number of clones, and entropy were associated with lower HR (P<0.05), although FDR were borderline significant (0.05<FDR<0.1). A model to predict EFS including baseline BCR evenness, ER status, stromal tumor-infiltrating lymphocyte level, and pCR was able to identify 3 groups with good, intermediate and poor prognosis (Kaplan-Meier 5-year EFS rates of 95%, 80% and 58%, respectively). Of note, about 63%, 45% and 12% of the patients achieved a pCR in the three prognostic groups, respectively. Conclusions: In the NeoALTTO trial, the presence of an evenly distributed BCR repertoire was associated with worse EFS. A model integrating baseline immune-related and clinical features was able to identify patients with excellent prognosis despite RD or, conversely, with poor prognosis after pCR. We envision that our model has the potential to allow the personalization of post-operative treatment strategies after both pCR and RD in HER2-positive BC. Further validation of our findings is warranted. Citation Format: Mattia Rediti, David Venet, Françoise Rothé, Tao Qing, Marion Maetens, Ian Bradbury, Miguel A. Izquierdo, Serena Di Cosimo, Florentine Hilbers, Mohammed Bajji, Nadia Harbeck, Michael Untch, David L. Rimm, Stephen Chia, Minetta C. Liu, Cristina Saura, Jens Huober, Paolo Nuciforo, Roberto Salgado, Sherene Loi, Lajos Pusztai, Christos Sotiriou. Predictive and prognostic role of T- and B-cell receptor repertoire in HER2-positive breast cancer: An analysis of the NeoALTTO clinical trial [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1998.

511 Background: Clinicopathological and molecular features, including estrogen receptor (ER) status and PAM50 subtypes, have shown an association with immunogenicity and tumor-infiltrating lymphocyte (TIL) levels in breast cancer (BC). To investigate the complexity of the immune response in HER2+ BC, we explored the association of T- and B-cell receptor (TCR and BCR) repertoires with clinicopathological characteristics, PAM50 subtypes and outcome in the NeoALTTO phase III trial. Methods: RNA sequencing (RNAseq) data from baseline tumor biopsies were available for 254 out of the 455 patients enrolled. TCR and BCR repertoires were extracted from RNAseq data using the MiXCR software. Repertoire and diversity measures (read counts, number of clones, evenness, Gini index, Shannon entropy, length of the complementarity-determining region 3 [CDR3], top and second top clone proportions) were estimated. PAM50 subtypes were computed from RNAseq data. Univariate and multivariate (adjusted for clinicopathological characteristics, TIL levels dichotomized using the median value of 12.5% and treatment arm) Cox proportional hazard models were used for survival analysis, while logistic regressions were used for pathological complete response (pCR), defined as ypT0/is. All results reported had a false discovery rate (FDR) <0.05. Results: Higher BCR read counts, number of clones and Gini index were significantly associated with ER-negative as well as grade 3 tumors. Among the PAM50 subtypes, HER2-enriched (HER2-E) showed significantly higher BCR read counts, number of clones and Gini index along with lower evenness compared to luminal A and B, as well as higher length of CDR3 than luminal A. Of note, basal-like showed similar BCR diversity measures to HER2-E. No significant differences were noted for TCR diversity measures. In multivariate analyses, neither TCR nor BCR features were associated with pCR, while BCR evenness (HR 1.5; 95%CI 1.1-2.1) and Gini index (HR 0.66; 95%CI 0.5-0.88) were associated with event-free survival. Conclusions: BCR repertoire measures suggest a clonal expansion in HER2-E and basal-like PAM50 subtypes. Furthermore, the implementation of BCR-derived biomarkers can help to identify patients with an improved clinical outcome after neoadjuvant anti-HER2 treatment. Our findings highlight the heterogeneity of the immune response within HER2+ BC and provide support for biomarker-driven treatment strategies including immunotherapy in this BC subtype. Further validation is required. Clinical trial information: NCT00553358 .

AbstractImmune checkpoint inhibitor therapies targeting PD‐1/PD‐L1 are now the standard of care in oncology across several hematologic and solid tumor types, including triple negative breast cancer (TNBC). Patients with metastatic or locally advanced TNBC with PD‐L1 expression on immune cells occupying ≥1% of tumor area demonstrated survival benefit with the addition of atezolizumab to nab‐paclitaxel. However, concerns regarding variability between immunohistochemical PD‐L1 assay performance and inter‐reader reproducibility have been raised. High tumor‐infiltrating lymphocytes (TILs) have also been associated with response to PD‐1/PD‐L1 inhibitors in patients with breast cancer (BC). TILs can be easily assessed on hematoxylin and eosin–stained slides and have shown reliable inter‐reader reproducibility. As an established prognostic factor in early stage TNBC, TILs are soon anticipated to be reported in daily practice in many pathology laboratories worldwide. Because TILs and PD‐L1 are parts of an immunological spectrum in BC, we propose the systematic implementation of combined PD‐L1 and TIL analyses as a more comprehensive immuno‐oncological biomarker for patient selection for PD‐1/PD‐L1 inhibition‐based therapy in patients with BC. Although practical and regulatory considerations differ by jurisdiction, the pathology community has the responsibility to patients to implement assays that lead to optimal patient selection. We propose herewith a risk‐management framework that may help mitigate the risks of suboptimal patient selection for immuno‐therapeutic approaches in clinical trials and daily practice based on combined TILs/PD‐L1 assessment in BC. © 2020 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Abstract Background: The level of immune suppression in an individual breast cancer (BC) patient is relevant for having a major benefit from treatments that act via or are directed to the immune response, such as anti-HER2 agents and immune checkpoint molecules, respectively. The link(s) between the tumor immune microenvironment and treatment responses has been investigated in HER2-positive BC. This study analyzed the association between baseline expression of genes regulating T cell activities (PD1, CTLA4, LAG3, TIM3, PDL1, PDL2, FOXP3, IL10, TGFβ (1-3), FOXP1 and other related genes) and pathologic complete response (pCR) rates in the NeoALTTO trial. Methods: NeoALTTO randomized early-stage HER2-positive BC patients to neoadjuvant trastuzumab (T), lapatinib (L) or T+L for 6 weeks followed by 12 weeks of concurrent paclitaxel. Anthracyclines were given after surgery (FEC). Patients with RNA sequencing data from their baseline tumor samples (N=254 patients out of 455 from the original cohort) were included in the current study. The primary endpoint, pCR, was defined as ypT0/is ypN0. Logistic regression was used for analysis of pCR. Cox regression univariate and multivariate (adjusted for clinicopathological parameters and treatment arms) analyses were performed. Results: In the total cohort analyzed, high PD1 (odds ratio, OR: 1.4, P=0.03), PDL2 (OR: 1.4, P=0.04), CTLA4 (OR: 1.4, P=0.03) and TGFβ3 (OR: 1.4, P=0.02) expression was associated with an increased probability of achieving pCR at the multivariate analysis. ERBB2 (HER2) expression was associated with pCR in the three arms (T: OR: 2.7, P=0.02; L: OR: 2.3, P=0.01; T+L: OR: 5, P&lt;0.001). Additionally in the T+L arm, PAM50 HER2-enriched subtype (OR: 2.9, P=0.03), as well as immune genes PD1 (OR: 2.1, P=0.01), PDL1 (OR: 1.8, P=0.03) and CTLA4 (OR: 2; P=0.01), were also associated with pCR. Conclusions: In the NeoALTTO population examined, PD1, PDL2, CTLA-4 and TGFβ3 expression at diagnosis were all associated with improved pCR rates after anti-HER2 treatment. The effect of these treatments seemed to be dependent on HER2 expression levels, which was particularly relevant in the T+L arm. These observations confirm previous findings that link immune infiltrates to higher pCR rates, demonstrate the clinical significance of the PD-1 pathway and additionally show that CTLA-4 and TGFβ3 could also be important in early stage HER2-positive BC. The association of tumors linked with immune regulatory gene expression with positive responses to neoadjuvant anti-HER2 agents suggests that they act in a way that reinvigorates the antitumor immunity in the face of tumor-mediated suppression. Patients whose tumors highly express these genes may be good candidates for immunotherapy before the start or after the neoadjuvant treatment when residual disease is detected at surgery, although these hypotheses require further confirmation. Citation Format: Cinzia Solinas, Pushpamali De Silva, David Venet, Soizic Garaud, Chunyan Gu-Trantien, Florentine Hilbers, Evandro de Azambuja, Olena Werner, Lorena De la Peña, Amylou Dueck, Serena Di Cosimo, Istvan Lang, Jens Huober, Sherko Küemmel, Carsten Denkert, Roberto Salgado, Christos Sotiriou, Martine Piccart-Gebhart, Debora Fumagalli, Karen Willard-Gallo. Immune regulatory gene expression and clinical outcome in the NeoALTTO trial [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3132.

Assessment of tumor-infiltrating lymphocytes (TILs) in histopathologic specimens can provide important prognostic information in diverse solid tumor types, and may also be of value in predicting response to treatments. However, implementation as a routine clinical biomarker has not yet been achieved. As successful use of immune checkpoint inhibitors and other forms of immunotherapy become a clinical reality, the need for widely applicable, accessible, and reliable immunooncology biomarkers is clear. In part 1 of this review we briefly discuss the host immune response to tumors and different approaches to TIL assessment. We propose a standardized methodology to assess TILs in solid tumors on hematoxylin and eosin sections, in both primary and metastatic settings, based on the International Immuno-Oncology Biomarker Working Group guidelines for TIL assessment in invasive breast carcinoma. A review of the literature regarding the value of TIL assessment in different solid tumor types follows in part 2. The method we propose is reproducible, affordable, easily applied, and has demonstrated prognostic and predictive significance in invasive breast carcinoma. This standardized methodology may be used as a reference against which other methods are compared, and should be evaluated for clinical validity and utility. Standardization of TIL assessment will help to improve consistency and reproducibility in this field, enrich both the quality and quantity of comparable evidence, and help to thoroughly evaluate the utility of TILs assessment in this era of immunotherapy.

Assessment of the immune response to tumors is growing in importance as the prognostic implications of this response are increasingly recognized, and as immunotherapies are evaluated and implemented in different tumor types. However, many different approaches can be used to assess and describe the immune response, which limits efforts at implementation as a routine clinical biomarker. In part 1 of this review, we have proposed a standardized methodology to assess tumor-infiltrating lymphocytes (TILs) in solid tumors, based on the International Immuno-Oncology Biomarkers Working Group guidelines for invasive breast carcinoma. In part 2 of this review, we discuss the available evidence for the prognostic and predictive value of TILs in common solid tumors, including carcinomas of the lung, gastrointestinal tract, genitourinary system, gynecologic system, and head and neck, as well as primary brain tumors, mesothelioma and melanoma. The particularities and different emphases in TIL assessment in different tumor types are discussed. The standardized methodology we propose can be adapted to different tumor types and may be used as a standard against which other approaches can be compared. Standardization of TIL assessment will help clinicians, researchers and pathologists to conclusively evaluate the utility of this simple biomarker in the current era of immunotherapy.