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

Combining Molecular and Traditional Prognostic Factors: A Holistic Approach to Breast Cancer Prognostication

Breast cancer is a heterogeneous disease with various morphologies and molecular features, and it is the second leading cause of cancer death in women in developed countries. According to the literature, we currently lack both prognostic biomarkers and therapeutic targets. The most important prognostic factors are disease stage and Nottingham grade. We conducted a retrospective analysis involving 273 patients with BC who underwent neoadjuvant therapy before proceeding to curative surgical treatment between 1 January 2014 and 31 December 2023. Pathological procedures were conducted at the Department of Pathology, Emergency County Hospital of Targu Mureș, Romania. A statistical analysis was performed. Regarding the relationship between Nottingham grade and Ki67, grade I was associated with a Ki67 of less than 14. The relationship between tumor grade and luminal was similar (p = 0.0001): Grade I was associated with luminal A. Regarding TNM stage, it was statistically significantly correlated with TILs (p = 0.01) and RCB (p = 0.0001). Stages III and IV were associated with a high RCB and poor prognosis. Regarding the prognostic value, Nottingham grade 3 and TNM stages III and IV were correlated with low overall survival and disease-free survival, with poor prognosis, and, among the molecular variables, RCB played the most important prognostic role.

Characteristics of auto-quantified tumor-infiltrating lymphocytes and the prognostic value in adenocarcinoma of the esophagogastric junction, gastric adenocarcinoma, and esophageal squamous cell carcinoma

BACKGROUND

Adenocarcinoma of the esophagogastric junction (AEGJ) with a specific pathological profile and poor prognosis has limited therapeutic options. Previous studies have found that TILs exhibit distinct characteristics in different tumors and are correlated with tumor prognosis. We established cellular training sets to obtain auto-quantified TILs in pathological images. And we compared the characteristics of TILs in AEGJ with those in esophageal squamous cell carcinoma (ESCC) and gastric adenocarcinoma (GAC) to gain insight into the unique immune environments of these three tumors and investigate the prognostic value of TILs in these three tumors.

METHODS

Utilizing a case-control study design, we analyzed 214 AEGJ, 256 GAC, and 752 ESCC cases. The TCGA dataset was used to validate prognostic value of auto-quantified TILs. The specific cellular training sets were established by experienced pathologists to determine TILs counts. Kruskal-Wallis test and multi-variable linear regression were conducted to explore TILs characteristics. Survival analyses were performed with Kaplan-Meier method and Cox proportional hazards model.

RESULTS

The three cellular training sets of these cancers achieved a classification accuracy of 87.55 at least. The median auto-quantified TILs of AEGJ, GAC, and ESCC cases were 4.82%, 1.92%, and 0.12%, respectively. The TILs demonstrated varied characteristics under distinctive clinicopathological traits. The higher TILs proportion was associated with better prognosis in esophagogastric cancers (all P < 0.05) and was an independent prognostic biomarker on AEGJ in both datasets (Taixing: HR = 0.965, 95% CI = 0.938-0.994; TCGA: HR = 0.811, 95% CI = 0.712-0.925).

CONCLUSIONS

We found variations in TILs across ESCC, GAC, and AEGJ, as assessed by image processing algorithms. Additionally, TILs in these three cancers are an independent prognostic factor. This enhances our understanding of the unique immune characteristics of the three tumors, improving patient outcomes.

Clinicopathological and molecular predictors of [18F]FDG-PET disease detection in HER2-positive early breast cancer: RESPONSE, a substudy of the randomized PHERGain trial

BACKGROUND

The PHERGain study (NCT03161353) is assessing early metabolic responses to neoadjuvant treatment with trastuzumab-pertuzumab and chemotherapy de-escalation using a [18Fluorine]fluorodeoxyglucose-positron emission tomography ([18F]FDG-PET) and a pathological complete response-adapted strategy in HER2-positive (HER2+) early breast cancer (EBC). Herein, we present RESPONSE, a PHERGain substudy, where clinicopathological and molecular predictors of [18F]FDG-PET disease detection were evaluated.

METHODS

A total of 500 patients with HER2 + EBC screened in the PHERGain trial with a tumor size > 1.5 cm by magnetic resonance imaging (MRI) were included in the RESPONSE substudy. PET[-] criteria entailed the absence of  ≥ 1 breast lesion with maximum standardized uptake value (SUVmax) ≥ 1.5 × SUVmean liver + 2 standard deviation. Among 75 PET[-] patients screened, 21 with SUVmax levels < 2.5 were randomly selected and matched with 21 PET[+] patients with SUVmax levels ≥ 2.5 based on patient characteristics associated with [18F]FDG-PET status. The association between baseline SUVmax and [18F]FDG-PET status ([-] or [+]) with clinicopathological characteristics was assessed. In addition, evaluation of stromal tumor-infiltrating lymphocytes (sTILs) and gene expression analysis using PAM50 and Vantage 3D™ Cancer Metabolism Panel were specifically compared in a matched cohort of excluded and enrolled patients based on the [18F]FDG-PET eligibility criteria.

RESULTS

Median SUVmax at baseline was 7.2 (range, 1-39.3). Among all analyzed patients, a higher SUVmax was associated with a higher tumor stage, larger tumor size, lymph node involvement, hormone receptor-negative status, higher HER2 protein expression, increased Ki67 proliferation index, and higher histological grade (p < 0.05). [18F]FDG-PET [-] criteria patients had smaller tumor size (p = 0.014) along with the absence of lymph node involvement and lower histological grade than [18F]FDG-PET [+] patients (p < 0.01). Although no difference in the levels of sTILs was found among 42 matched [18F]FDG-PET [-]/[+] criteria patients (p = 0.73), [18F]FDG-PET [-] criteria patients showed a decreased risk of recurrence (ROR) and a lower proportion of PAM50 HER2-enriched subtype than [18F]FDG-PET[+] patients (p < 0.05). Differences in the expression of genes involved in cancer metabolism were observed between [18F]FDG-PET [-] and [18F]FDG-PET[+] criteria patients.

CONCLUSIONS

These results highlight the clinical, biological, and metabolic heterogeneity of HER2+ breast cancer, which may facilitate the selection of HER2+ EBC patients likely to benefit from [18F]FDG-PET imaging as a tool to guide therapy.

TRIAL REGISTRATION

Clinicaltrials.gov; NCT03161353; registration date: May 15, 2017.

A panoptic segmentation dataset and deep-learning approach for explainable scoring of tumor-infiltrating lymphocytes

Tumor-Infiltrating Lymphocytes (TILs) have strong prognostic and predictive value in breast cancer, but their visual assessment is subjective. To improve reproducibility, the International Immuno-oncology Working Group recently released recommendations for the computational assessment of TILs that build on visual scoring guidelines. However, existing resources do not adequately address these recommendations due to the lack of annotation datasets that enable joint, panoptic segmentation of tissue regions and cells. Moreover, existing deep-learning methods focus entirely on either tissue segmentation or cell nuclei detection, which complicates the process of TILs assessment by necessitating the use of multiple models and reconciling inconsistent predictions. We introduce PanopTILs, a region and cell-level annotation dataset containing 814,886 nuclei from 151 patients, openly accessible at: sites.google.com/view/panoptils . Using PanopTILs we developed MuTILs, a neural network optimized for assessing TILs in accordance with clinical recommendations. MuTILs is a concept bottleneck model designed to be interpretable and to encourage sensible predictions at multiple resolutions. Using a rigorous internal-external cross-validation procedure, MuTILs achieves an AUROC of 0.93 for lymphocyte detection and a DICE coefficient of 0.81 for tumor-associated stroma segmentation. Our computational score closely matched visual scores from 2 pathologists (Spearman R = 0.58-0.61, p < 0.001). Moreover, computational TILs scores had a higher prognostic value than visual scores, independent of TNM stage and patient age. In conclusion, we introduce a comprehensive open data resource and a modeling approach for detailed mapping of the breast tumor microenvironment.

Tumor-Infiltrating Lymphocytes in Patients With Stage I Triple-Negative Breast Cancer Untreated With Chemotherapy

Importance

The absolute benefit of chemotherapy for all patients with stage I triple-negative breast cancer (TNBC) is unclear, and biomarkers are not currently available for selecting patients with an excellent outcome for whom neoadjuvant or adjuvant chemotherapy may have negligible benefit. High levels of stromal tumor-infiltrating lymphocytes (sTILs) are associated with favorable survival in TNBC, but data solely in stage I TNBC are lacking.

Objective

To examine the outcomes of patients of all ages with stage I TNBC solely and who received neither neoadjuvant nor adjuvant chemotherapy, according to centrally reviewed sTIL levels at prespecified cutoffs.

Design, Setting, and Participants

This cohort study used the Netherlands Cancer Registry to identify patients diagnosed with stage I TNBC between January 1, 2005, and December 31, 2015, who were not treated with chemotherapy. Only patients who did not receive neoadjuvant and/or adjuvant chemotherapy were selected. The clinical data were matched with their corresponding pathology data provided by the Dutch Pathology Registry. Data analysis was performed between February and October 2023.

Main Outcomes and Measures

The primary end point was breast cancer-specific survival (BCSS) at 5, 10, and 15 years for the prespecified sTIL level cutoffs of 30%, 50%, and 75%. Hematoxylin and eosin-stained slides were used for central review of histologic subtype, grade, and lymphovascular invasion. The International Immuno-Oncology Biomarker Working Group guidelines were used to score the sTIL levels; these levels were determined for 1041 patients.

Results

A total of 4511 females with stage I TNBC (mean [SD] age at diagnosis, 64.4 [11.1] years; median follow-up, 11.4 [95% CI, 10.9-11.9] years) were included. Most tumors (952 [91.5%]) were invasive carcinomas of nonspecial histologic subtype. Most patients (548 [52.6%]) had pT1cN0 tumors. Median (range) sTIL level was 5% (1%-99%). A total of 775 patients (74.4%) had sTIL levels below 30%, 266 (25.6%) had 30% or greater, 203 (19.5%) had 50% or greater, and 141 (13.5%) had 75% or greater. Patients with pT1abN0 tumors had a more favorable outcome vs patients with pT1cN0 tumors, with a 10-year BCSS of 92% (95% CI, 89%-94%) vs 86% (95% CI, 82%-89%). In the overall cohort, sTIL levels of at least 30% were associated with better BCSS compared with sTIL levels less than 30% (96% and 87%, respectively; hazard ratio [HR], 0.45; 95% CI, 0.26-0.77). High sTIL levels of 50% or greater were associated with a better outcome than low sTIL levels of less than 50% (HR, 0.27; 95% CI, 0.10-0.74) in patients with pT1C tumors, with a 10-year BCSS of 95% increasing to 98% with sTIL levels of 75% or greater.

Conclusions and Relevance

Results of this study showed that patients with stage I TNBC and high level of sTILs who did not receive neoadjuvant or adjuvant chemotherapy had excellent 10-year BCSS. The findings further support the role of sTILs as integral biomarkers in prospective clinical trials of therapy optimization for this patient population.

Emerging measurements for tumor-infiltrating lymphocytes in breast cancer

Tumor-infiltrating lymphocytes are a general term for lymphocytes or immune cells infiltrating the tumor microenvironment. Numerous studies have demonstrated tumor-infiltrating lymphocytes to be robust prognostic and predictive biomarkers in breast cancer. Recently, immune checkpoint inhibitors, which directly target tumor-infiltrating lymphocytes, have become part of standard of care treatment for triple-negative breast cancer. Surprisingly, tumor-infiltrating lymphocytes quantified by conventional methods do not predict response to immune checkpoint inhibitors, which highlights the heterogeneity of tumor-infiltrating lymphocytes and the complexity of the immune network in the tumor microenvironment. Tumor-infiltrating lymphocytes are composed of diverse immune cell populations, including cytotoxic CD8-positive T lymphocytes, B cells and myeloid cells. Traditionally, tumor-infiltrating lymphocytes in tumor stroma have been evaluated by histology. However, the standardization of this approach is limited, necessitating the use of various novel technologies to elucidate the heterogeneity in the tumor microenvironment. This review outlines the evaluation methods for tumor-infiltrating lymphocytes from conventional pathological approaches that evaluate intratumoral and stromal tumor-infiltrating lymphocytes such as immunohistochemistry, to the more recent advancements in computer tissue imaging using artificial intelligence, flow cytometry sorting and multi-omics analyses using high-throughput assays to estimate tumor-infiltrating lymphocytes from bulk tumor using immune signatures or deconvolution tools. We also discuss higher resolution technologies that enable the analysis of tumor-infiltrating lymphocytes heterogeneity such as single-cell analysis and spatial transcriptomics. As we approach the era of personalized medicine, it is important for clinicians to understand these technologies.

Automated tumor immunophenotyping predicts clinical benefit from anti-PD-L1 immunotherapy

Cancer immunotherapy has transformed the clinical approach to patients with malignancies, as profound benefits can be seen in a subset of patients. To identify this subset, biomarker analyses increasingly focus on phenotypic and functional evaluation of the tumor microenvironment to determine if density, spatial distribution, and cellular composition of immune cell infiltrates can provide prognostic and/or predictive information. Attempts have been made to develop standardized methods to evaluate immune infiltrates in the routine assessment of certain tumor types; however, broad adoption of this approach in clinical decision-making is still missing. We developed approaches to categorize solid tumors into 'desert', 'excluded', and 'inflamed' types according to the spatial distribution of CD8+ immune effector cells to determine the prognostic and/or predictive implications of such labels. To overcome the limitations of this subjective approach, we incrementally developed four automated analysis pipelines of increasing granularity and complexity for density and pattern assessment of immune effector cells. We show that categorization based on 'manual' observation is predictive for clinical benefit from anti-programmed death ligand 1 therapy in two large cohorts of patients with non-small cell lung cancer or triple-negative breast cancer. For the automated analysis we demonstrate that a combined approach outperforms individual pipelines and successfully relates spatial features to pathologist-based readouts and the patient's response to therapy. Our findings suggest that tumor immunophenotype generated by automated analysis pipelines should be evaluated further as potential predictive biomarkers for cancer immunotherapy. © 2024 The Pathological Society of Great Britain and Ireland.

Tumor-Infiltrating Lymphocytes Refine Outcomes in Triple-Negative Breast Cancer Treated with Anthracycline-Free Neoadjuvant Chemotherapy

PURPOSE

Stromal tumor-infiltrating lymphocytes (sTIL) are associated with pathologic complete response (pCR) and long-term outcomes for triple-negative breast cancer (TNBC) in the setting of anthracycline-based chemotherapy. The impact of sTILs on refining outcomes beyond prognostic information provided by pCR in anthracycline-free neoadjuvant chemotherapy (NAC) is not known.

EXPERIMENTAL DESIGN

This is a pooled analysis of two studies where patients with stage I (T>1 cm)-III TNBC received carboplatin (AUC 6) plus docetaxel (75 mg/m2; CbD) NAC. sTILs were evaluated centrally on pre-treatment hematoxylin and eosin slides using standard criteria. Cox regression analysis was used to examine the effect of variables on event-free survival (EFS) and overall survival (OS).

RESULTS

Among 474 patients, 44% had node-positive disease. Median sTILs were 5% (range, 1%-95%), and 32% of patients had ≥30% sTILs. pCR rate was 51%. On multivariable analysis, T stage (OR, 2.08; P = 0.007), nodal status (OR, 1.64; P = 0.035), and sTILs (OR, 1.10; P = 0.011) were associated with pCR. On multivariate analysis, nodal status (HR, 0.46; P = 0.008), pCR (HR, 0.20; P < 0.001), and sTILs (HR, 0.95; P = 0.049) were associated with OS. At 30% cut-point, sTILs stratified outcomes in stage III disease, with 5-year OS 86% versus 57% in ≥30% versus <30% sTILs (HR, 0.29; P = 0.014), and numeric trend in stage II, with 5-year OS 93% versus 89% in ≥30% versus <30% sTILs (HR, 0.55; P = 0.179). Among stage II-III patients with pCR, EFS was better in those with ≥30% sTILs (HR, 0.16; P, 0.047).

CONCLUSIONS

sTILs density was an independent predictor of OS beyond clinicopathologic features and pathologic response in patients with TNBC treated with anthracycline-free CbD chemotherapy. Notably, sTILs density stratified outcomes beyond tumor-node-metastasis (TNM) stage and pathologic response. These findings highlight the role of sTILs in patient selection and stratification for neo/adjuvant escalation and de-escalation strategies.

Training pathologists to assess stromal tumour-infiltrating lymphocytes in breast cancer synergises efforts in clinical care and scientific research

A growing body of research supports stromal tumour-infiltrating lymphocyte (TIL) density in breast cancer to be a robust prognostic and predicive biomarker. The gold standard for stromal TIL density quantitation in breast cancer is pathologist visual assessment using haematoxylin and eosin-stained slides. Artificial intelligence/machine-learning algorithms are in development to automate the stromal TIL scoring process, and must be validated against a reference standard such as pathologist visual assessment. Visual TIL assessment may suffer from significant interobserver variability. To improve interobserver agreement, regulatory science experts at the US Food and Drug Administration partnered with academic pathologists internationally to create a freely available online continuing medical education (CME) course to train pathologists in assessing breast cancer stromal TILs using an interactive format with expert commentary. Here we describe and provide a user guide to this CME course, whose content was designed to improve pathologist accuracy in scoring breast cancer TILs. We also suggest subsequent steps to translate knowledge into clinical practice with proficiency testing.

Longitudinal Assessment of Tumor-Infiltrating Lymphocytes in Primary Breast Cancer Following Neoadjuvant Radiation Therapy

PURPOSE

Tumor-infiltrating lymphocytes (TILs) have prognostic significance in several cancers, including breast cancer. Despite interest in combining radiation therapy with immunotherapy, little is known about the effect of radiation therapy itself on the tumor-immune microenvironment, including TILs. Here, we interrogated longitudinal dynamics of TILs and systemic lymphocytes in patient samples taken before, during, and after neoadjuvant radiation therapy (NART) from PRADA and Neo-RT breast clinical trials.

METHODS AND MATERIALS

We manually scored stromal TILs (sTILs) from longitudinal tumor samples using standardized guidelines as well as deep learning-based scores at cell-level (cTIL) and cell- and tissue-level combination analyses (SuperTIL). In parallel, we interrogated absolute lymphocyte counts from routine blood tests at corresponding time points during treatment. Exploratory analyses studied the relationship between TILs and pathologic complete response (pCR) and long-term outcomes.

RESULTS

Patients receiving NART experienced a significant and uniform decrease in sTILs that did not recover at the time of surgery (P < .0001). This lymphodepletive effect was also mirrored in peripheral blood. Our SuperTIL deep learning score showed good concordance with manual sTILs and importantly performed comparably to manual scores in predicting pCR from diagnostic biopsies. The analysis suggested an association between baseline sTILs and pCR, as well as sTILs at surgery and relapse, in patients receiving NART.

CONCLUSIONS

This study provides novel insights into TIL dynamics in the context of NART in breast cancer and demonstrates the potential for artificial intelligence to assist routine pathology. We have identified trends that warrant further interrogation and have a bearing on future radioimmunotherapy trials.

Prognostic Value of "Basal-like" Morphology, Tumor-Infiltrating Lymphocytes and Multi-MAGE-A Expression in Triple-Negative Breast Cancer

"Basal-like" (BL) morphology and the expression of cancer testis antigens (CTA) in breast cancer still have unclear prognostic significance. The aim of our research was to explore correlations of the morphological characteristics and tumor microenvironment in triple-negative breast carcinomas (TNBCs) with multi-MAGE-A CTA expression and to determine their prognostic significance. Clinical records of breast cancer patients who underwent surgery between January 2017 and December 2018 in four major Croatian clinical centers were analyzed. A total of 97 non-metastatic TNBCs with available tissue samples and treatment information were identified. Cancer tissue sections were additionally stained with programmed death-ligand 1 (PD-L1) Ventana (SP142) and multi-MAGE-A (mAb 57B). BL morphology was detected in 47 (49%) TNBCs and was associated with a higher Ki-67 proliferation index and histologic grade. Expression of multi-MAGE-A was observed in 77 (79%) TNBCs and was significantly associated with BL morphology. Lymphocyte-predominant breast cancer (LPBC) status was detected in 11 cases (11.3%) and significantly correlated with the Ki-67 proliferation index, increased number of intratumoral lymphocytes (itTIL), and PD-L1 expression. No impact of BL morphology, multi-MAGE-A expression, histologic type, or LPBC status on disease-free survival was observed. Our data suggest that tumor morphology could help identify patients with potential benefits from CTA-targeting immunotherapy.

Tumor-infiltrating lymphocytes in HER2-positive breast cancer treated with neoadjuvant chemotherapy and dual HER2-blockade

Tumor-infiltrating lymphocytes (TILs) have been associated with outcomes in HER2-positive breast cancer patients treated with neoadjuvant chemotherapy and trastuzumab. However, it remains unclear if TILs could be a prognostic and/or predictive biomarker in the context of dual HER2-targeting treatment. In this study, we evaluated the association between TILs and pathological response (pCR) and invasive-disease free survival (IDFS) in 389 patients with stage II-III HER2 positive breast cancer who received neoadjuvant anthracycline-containing or anthracycline-free chemotherapy combined with trastuzumab and pertuzumab in the TRAIN-2 trial. Although no significant association was seen between TILs and pCR, patients with TIL scores ≥60% demonstrated an excellent 3-year IDFS of 100% (95% CI 100-100), regardless of hormone receptor status, nodal stage and attainment of pCR. Additionally, in patients with hormone receptor positive disease, TILs as a continuous variable showed a trend to a positive association with pCR (adjusted Odds Ratio per 10% increase in TILs 1.15, 95% CI 0.99-1.34, p = 0.070) and IDFS (adjusted Hazard Ratio per 10% increase in TILs 0.71, 95% CI 0.50-1.01, p = 0.058). We found no interactions between TILs and anthracycline treatment. Our results suggest that high TIL scores might be able to identify stage II-III HER2-positive breast cancer patients with a favorable prognosis.

Evaluation of the prognostic potential of histopathological subtyping in high-grade serous ovarian carcinoma

High-grade serous ovarian carcinoma (HGSOC) can be categorized into four gene expression-based subtypes, with supposedly distinct prognoses and treatment responses. Murakami et al. translated these gene expression-based subtypes into the histopathological mesenchymal, immunoreactive, solid and proliferative, and papilloglandular subtypes, showing differences in survival outcomes. Miyagawa et al. refined these criteria to improve the interobserver concordance. The current retrospective study evaluated the interobserver variability and the prognostic differences between the histopathologic subtypes using the criteria of both Murakami et al. and Miyagawa et al. in 208 HGSOC cases. The mesenchymal subtype was considered first, followed by the immunoreactive subtype. Non-conforming cases were categorized as solid and proliferative or papilloglandular. The mesenchymal subtype was identified in 122 patients (58.7%) for both criteria. Using the criteria of Murakami et al., 10 cases (4.8%) were immunoreactive, 26 (12.5%) solid and proliferative, and 50 (24%) papilloglandular, with a concordance rate of 62.5% (κ = 0.34, p < .001). Using the Miyagawa et al. criteria, 23 cases (11%) were immunoreactive, 20 (9.6%) solid and proliferative, and 43 (20.7%) papilloglandular. No survival differences were observed between the subtypes. The fair reproducibility of the histopathological subtype classification of HGSOC and the lack of survival differences among these subtypes indicate the need for further refinement of the criteria and exploration of their correlation with overall survival outcomes before clinical application.

Tumor-Infiltrating Lymphocytes in Triple-Negative Breast Cancer

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.

Image-based multiplex immune profiling of cancer tissues: translational implications. A report of the International Immuno-oncology Biomarker Working Group on Breast Cancer

Recent advances in the field of immuno-oncology have brought transformative changes in the management of cancer patients. The immune profile of tumours has been found to have key value in predicting disease prognosis and treatment response in various cancers. Multiplex immunohistochemistry and immunofluorescence have emerged as potent tools for the simultaneous detection of multiple protein biomarkers in a single tissue section, thereby expanding opportunities for molecular and immune profiling while preserving tissue samples. By establishing the phenotype of individual tumour cells when distributed within a mixed cell population, the identification of clinically relevant biomarkers with high-throughput multiplex immunophenotyping of tumour samples has great potential to guide appropriate treatment choices. Moreover, the emergence of novel multi-marker imaging approaches can now provide unprecedented insights into the tumour microenvironment, including the potential interplay between various cell types. However, there are significant challenges to widespread integration of these technologies in daily research and clinical practice. This review addresses the challenges and potential solutions within a structured framework of action from a regulatory and clinical trial perspective. New developments within the field of immunophenotyping using multiplexed tissue imaging platforms and associated digital pathology are also described, with a specific focus on translational implications across different subtypes of cancer. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

A real-world clinicopathological model for predicting pathological complete response to neoadjuvant chemotherapy in breast cancer

Purpose

This study aimed to develop and validate a clinicopathological model to predict pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer patients and identify key prognostic factors.

Methods

This retrospective study analyzed data from 279 breast cancer patients who received NAC at Zhejiang Provincial People's Hospital from 2011 to 2021. Additionally, an external validation dataset, comprising 50 patients from Lanxi People's Hospital and Second Affiliated Hospital, Zhejiang University School of Medicine from 2022 to 2023 was utilized for model verification. A multivariate logistic regression model was established incorporating clinical, ultrasound features, circulating tumor cells (CTCs), and pathology variables at baseline and post-NAC. Model performance for predicting pCR was evaluated. Prognostic factors were identified using survival analysis.

Results

In the 279 patients enrolled, a pathologic complete response (pCR) rate of 27.96% (78 out of 279) was achieved. The predictive model incorporated independent predictors such as stromal tumor-infiltrating lymphocyte (sTIL) levels, Ki-67 expression, molecular subtype, and ultrasound echo features. The model demonstrated strong predictive accuracy for pCR (C-statistics/AUC 0.874), especially in human epidermal growth factor receptor 2 (HER2)-enriched (C-statistics/AUC 0.878) and triple-negative (C-statistics/AUC 0.870) subtypes, and the model performed well in external validation data set (C-statistics/AUC 0.836). Incorporating circulating tumor cell (CTC) changes post-NAC and tumor size changes further improved predictive performance (C-statistics/AUC 0.945) in the CTC detection subgroup. Key prognostic factors included tumor size >5cm, lymph node metastasis, sTIL levels, estrogen receptor (ER) status and pCR. Despite varied pCR rates, overall prognosis after standard systemic therapy was consistent across molecular subtypes.

Conclusion

The developed predictive model showcases robust performance in forecasting pCR in NAC-treated breast cancer patients, marking a step toward more personalized therapeutic strategies in breast cancer.

Evaluation of tumor-infiltrating lymphocytes, PD-L1, and PIK3CA mutations and association with prognosis in HER2-positive early stage breast cancer

BACKGROUND

Tumor-infiltrating lymphocytes (TILs) have predictive and prognostic potential in HER2-positive breast cancer (HER2+ BC). Programmed death-ligand 1 (PD-L1) is an immune checkpoint protein, with important roles in the tumor microenvironment, possibly in both tumor and immune cells (ICs), providing rationale for targeting with immune-checkpoint therapy. PIK3CA mutations are oncogenic, activating mutations, which are also of relevance in breast cancer. Herein, we investigate the frequency of TILs, PD-L1 and PIK3CA mutations, and whether these factors influence outcome, in early HER2+ BC.

MATERIALS AND METHODS

Stromal TILs (sTILs) and PD-L1 expressions were assessed using full tumor-sections and TMA, respectively, from 236 patients with HER2+ BC. TILs were assessed, according to a standardized method, as continuous measurement and according to three predefined categories: low (0-10%), intermediate (11-59%), and high (60-100%). PD-L1 immunohistochemistry (Ventana SP263) was evaluated and positivity defined as ≥1% expression in tumor and ICs. PIK3CA mutations (exons 9 and 20) were determined by pyrosequencing.

RESULTS

Fourteen percent of patients had high sTILs and 25% had a PIK3CA mutation. PD-L1 expression was more frequent in ICs (68%) than tumor cells (24%). Patients with low sTILs had a significantly worse overall survival (multivariate: HR 2.80; 95% CI 1.36-5.78; p = .02).

DISCUSSION

Patients with low sTILs had a significantly poorer survival, despite adequate treatment with adjuvant therapy.

Evaluation of tumor-infiltrating lymphocytes and mammographic density as predictors of response to neoadjuvant systemic therapy in breast cancer

BACKGROUND

Response rates vary among breast cancer patients treated with neoadjuvant systemic therapy (NAST). Thus, there is a need for reliable treatment predictors. Evidence suggests tumor-infiltrating lymphocytes (TILs) predict NAST response. Still, TILs are seldom used clinically as a treatment determinant. Mammographic density (MD) is another potential marker for NAST benefit and its relationship with TILs is unknown. Our aims were to investigate TILs and MD as predictors of NAST response and to study the unexplored relationship between TILs and MD.

MATERIAL AND METHODS

We studied 315 invasive breast carcinomas treated with NAST between 2013 and 2020. Clinicopathological data were retrieved from medical records. The endpoint was defined as pathological complete response (pCR) in the breast. TILs were evaluated in pre-treatment core biopsies and categorized as high (≥10%) or low (<10%). MD was scored (a-d) according to the breast imaging reporting and data system (BI-RADS) fifth edition. Binary logistic regression and Spearman's test of correlation were performed using SPSS.

RESULTS

Out of 315 carcinomas, 136 achieved pCR. 94 carcinomas had high TILs and 215 had low TILs. Six carcinomas had no available TIL data. The number of carcinomas in each BI-RADS category were 37, 122, 112, and 44 for a, b, c, and d, respectively. High TILs were independently associated with pCR (OR: 2.95; 95% CI: 1.59-5.46) compared to low TILs. In the univariable analysis, MD (BI-RADS d vs. a) showed a tendency of higher likelihood for pCR (OR: 2.43; 95% CI: 0.99-5.98). However, the association was non-significant, which is consistent with the result of the multivariable analysis (OR: 2.51; 95% CI: 0.78-8.04). We found no correlation between TILs and MD (0.02; p = .80).

CONCLUSION

TILs significantly predicted NAST response. We could not define MD as a significant predictor of NAST response. These findings should be further replicated.

PROACTING: predicting pathological complete response to neoadjuvant chemotherapy in breast cancer from routine diagnostic histopathology biopsies with deep learning

BACKGROUND

Invasive breast cancer patients are increasingly being treated with neoadjuvant chemotherapy; however, only a fraction of the patients respond to it completely. To prevent overtreatment, there is an urgent need for biomarkers to predict treatment response before administering the therapy.

METHODS

In this retrospective study, we developed hypothesis-driven interpretable biomarkers based on deep learning, to predict the pathological complete response (pCR, i.e., the absence of tumor cells in the surgical resection specimens) to neoadjuvant chemotherapy solely using digital pathology H&E images of pre-treatment breast biopsies. Our approach consists of two steps: First, we use deep learning to characterize aspects of the tumor micro-environment by detecting mitoses and segmenting tissue into several morphology compartments including tumor, lymphocytes and stroma. Second, we derive computational biomarkers from the segmentation and detection output to encode slide-level relationships of components of the tumor microenvironment, such as tumor and mitoses, stroma, and tumor infiltrating lymphocytes (TILs).

RESULTS

We developed and evaluated our method on slides from n = 721 patients from three European medical centers with triple-negative and Luminal B breast cancers and performed external independent validation on n = 126 patients from a public dataset. We report the predictive value of the investigated biomarkers for predicting pCR with areas under the receiver operating characteristic curve between 0.66 and 0.88 across the tested cohorts.

CONCLUSION

The proposed computational biomarkers predict pCR, but will require more evaluation and finetuning for clinical application. Our results further corroborate the potential role of deep learning to automate TILs quantification, and their predictive value in breast cancer neoadjuvant treatment planning, along with automated mitoses quantification. We made our method publicly available to extract segmentation-based biomarkers for research purposes.

Evaluation of tumour infiltrating lymphocytes in luminal breast cancer using artificial intelligence

BACKGROUND

Tumour infiltrating lymphocytes (TILs) are a prognostic parameter in triple-negative and human epidermal growth factor receptor 2 (HER2)-positive breast cancer (BC). However, their role in luminal (oestrogen receptor positive and HER2 negative (ER + /HER2-)) BC remains unclear. In this study, we used artificial intelligence (AI) to assess the prognostic significance of TILs in a large well-characterised cohort of luminal BC.

METHODS

Supervised deep learning model analysis of Haematoxylin and Eosin (H&E)-stained whole slide images (WSI) was applied to a cohort of 2231 luminal early-stage BC patients with long-term follow-up. Stromal TILs (sTILs) and intratumoural TILs (tTILs) were quantified and their spatial distribution within tumour tissue, as well as the proportion of stroma involved by sTILs were assessed. The association of TILs with clinicopathological parameters and patient outcome was determined.

RESULTS

A strong positive linear correlation was observed between sTILs and tTILs. High sTILs and tTILs counts, as well as their proximity to stromal and tumour cells (co-occurrence) were associated with poor clinical outcomes and unfavourable clinicopathological parameters including high tumour grade, lymph node metastasis, large tumour size, and young age. AI-based assessment of the proportion of stroma composed of sTILs (as assessed visually in routine practice) was not predictive of patient outcome. tTILs was an independent predictor of worse patient outcome in multivariate Cox Regression analysis.

CONCLUSION

AI-based detection of TILs counts, and their spatial distribution provides prognostic value in luminal early-stage BC patients. The utilisation of AI algorithms could provide a comprehensive assessment of TILs as a morphological variable in WSIs beyond eyeballing assessment.

B-cells and regulatory T-cells in the microenvironment of HER2+ breast cancer are associated with decreased survival: a real-world analysis of women with HER2+ metastatic breast cancer

BACKGROUND

Despite major improvements in treatment of HER2-positive metastatic breast cancer (MBC), only few patients achieve complete remission and remain progression free for a prolonged time. The tumor immune microenvironment plays an important role in the response to treatment in HER2-positive breast cancer and could contain valuable prognostic information. Detailed information on the cancer-immune cell interactions in HER2-positive MBC is however still lacking. By characterizing the tumor immune microenvironment in patients with HER2-positive MBC, we aimed to get a better understanding why overall survival (OS) differs so widely and which alternative treatment approaches may improve outcome.

METHODS

We included all patients with HER2-positive MBC who were treated with trastuzumab-based palliative therapy in the Netherlands Cancer Institute between 2000 and 2014 and for whom pre-treatment tissue from the primary tumor or from metastases was available. Infiltrating immune cells and their spatial relationships to one another and to tumor cells were characterized by immunohistochemistry and multiplex immunofluorescence. We also evaluated immune signatures and other key pathways using next-generation RNA-sequencing data. With nine years median follow-up from initial diagnosis of MBC, we investigated the association between tumor and immune characteristics and outcome.

RESULTS

A total of 124 patients with 147 samples were included and evaluated. The different technologies showed high correlations between each other. T-cells were less prevalent in metastases compared to primary tumors, whereas B-cells and regulatory T-cells (Tregs) were comparable between primary tumors and metastases. Stromal tumor-infiltrating lymphocytes in general were not associated with OS. The infiltration of B-cells and Tregs in the primary tumor was associated with unfavorable OS. Four signatures classifying the extracellular matrix of primary tumors showed differential survival in the population as a whole.

CONCLUSIONS

In a real-world cohort of 124 patients with HER2-positive MBC, B-cells, and Tregs in primary tumors are associated with unfavorable survival. With this paper, we provide a comprehensive insight in the tumor immune microenvironment that could guide further research into development of novel immunomodulatory strategies.

Artificial Intelligence in Breast Cancer Diagnosis and Personalized Medicine

Breast cancer is a significant cause of cancer-related mortality in women worldwide. Early and precise diagnosis is crucial, and clinical outcomes can be markedly enhanced. The rise of artificial intelligence (AI) has ushered in a new era, notably in image analysis, paving the way for major advancements in breast cancer diagnosis and individualized treatment regimens. In the diagnostic workflow for patients with breast cancer, the role of AI encompasses screening, diagnosis, staging, biomarker evaluation, prognostication, and therapeutic response prediction. Although its potential is immense, its complete integration into clinical practice is challenging. Particularly, these challenges include the imperatives for extensive clinical validation, model generalizability, navigating the "black-box" conundrum, and pragmatic considerations of embedding AI into everyday clinical environments. In this review, we comprehensively explored the diverse applications of AI in breast cancer care, underlining its transformative promise and existing impediments. In radiology, we specifically address AI in mammography, tomosynthesis, risk prediction models, and supplementary imaging methods, including magnetic resonance imaging and ultrasound. In pathology, our focus is on AI applications for pathologic diagnosis, evaluation of biomarkers, and predictions related to genetic alterations, treatment response, and prognosis in the context of breast cancer diagnosis and treatment. Our discussion underscores the transformative potential of AI in breast cancer management and emphasizes the importance of focused research to realize the full spectrum of benefits of AI in patient care.

Deep learning model improves tumor-infiltrating lymphocyte evaluation and therapeutic response prediction in breast cancer

Tumor-infiltrating lymphocytes (TILs) have been recognized as key players in the tumor microenvironment of breast cancer, but substantial interobserver variability among pathologists has impeded its utility as a biomarker. We developed a deep learning (DL)-based TIL analyzer to evaluate stromal TILs (sTILs) in breast cancer. Three pathologists evaluated 402 whole slide images of breast cancer and interpreted the sTIL scores. A standalone performance of the DL model was evaluated in the 210 cases (52.2%) exhibiting sTIL score differences of less than 10 percentage points, yielding a concordance correlation coefficient of 0.755 (95% confidence interval [CI], 0.693-0.805) in comparison to the pathologists' scores. For the 226 slides (56.2%) showing a 10 percentage points or greater variance between pathologists and the DL model, revisions were made. The number of discordant cases was reduced to 116 (28.9%) with the DL assistance (p < 0.001). The DL assistance also increased the concordance correlation coefficient of the sTIL score among every two pathologists. In triple-negative and human epidermal growth factor receptor 2 (HER2)-positive breast cancer patients who underwent the neoadjuvant chemotherapy, the DL-assisted revision notably accentuated higher sTIL scores in responders (26.8 ± 19.6 vs. 19.0 ± 16.4, p = 0.003). Furthermore, the DL-assistant revision disclosed the correlation of sTIL-high tumors (sTIL ≥ 50) with the chemotherapeutic response (odd ratio 1.28 [95% confidence interval, 1.01-1.63], p = 0.039). Through enhancing inter-pathologist concordance in sTIL interpretation and predicting neoadjuvant chemotherapy response, here we report the utility of the DL-based tool as a reference for sTIL scoring in breast cancer assessment.

PD-L1 protein expression in breast cancer

AIMS

The immune checkpoint marker, Programmed cell death-ligand 1 (PD-L1), is expressed by both cancer epithelial cells and tumour-infiltrating immune cells (TICs) thus constituting a potential target for immunotherapy. This is of particular interest in triple negative breast cancer. In this study, we assessed the prognostic value of PD-L1 expression in tumour epithelial cells and TICs in a series of patients with breast cancer with long-term follow-up, and associations between PD-L1 expression and histopathological type and grade, proliferation and molecular subtype.

METHODS

Using immunohistochemistry for PD-L1 in tissue microarrays, we assessed PD-L1 expression in 821 tumours. Expression of PD-L1 was assessed separately in the epithelial and stromal compartments and classified as <1%, ≥1% to <10% or ≥10% positive staining cells. We correlated PD-L1 expression in tumour epithelial cells and TICs with tumour characteristics using Pearson's χ2 test, and prognosis by cumulative incidence of death from breast cancer and Cox regression analyses.

RESULTS

We found membranous staining in ≥1% of tumour epithelial cells in 53/821 cases (6.5%). Of these, 21 (2.6%) were ≥10%. Among TICs, staining (≥1%) was seen in 144/821 cases (17.6%). Of these, 62 were ≥10% (7.6%). PD-L1 was associated with high histopathological grade and proliferation, and the medullary and metaplastic patterns. In TICs, PD-L1 ≥1% found in 22/34 (34.4%) human epidermal growth factor receptor 2 type and 29/58 (50%) basal phenotype. An independent association between PD-L1 expression and prognosis was not observed.

CONCLUSIONS

PD-L1 is expressed more frequently in TICs than tumour epithelial cells. Expression in TICs is associated with aggressive tumour characteristics and non-luminal tumours but not with prognosis.

Pitfalls in machine learning-based assessment of tumor-infiltrating lymphocytes in breast cancer: A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer

The clinical significance of the tumor-immune interaction in breast cancer is now established, and tumor-infiltrating lymphocytes (TILs) have emerged as predictive and prognostic biomarkers for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2-negative) breast cancer and HER2-positive breast cancer. How computational assessments of TILs might complement manual TIL assessment in trial and daily practices is currently debated. Recent efforts to use machine learning (ML) to automatically evaluate TILs have shown promising results. We review state-of-the-art approaches and identify pitfalls and challenges of automated TIL evaluation by studying the root cause of ML discordances in comparison to manual TIL quantification. We categorize our findings into four main topics: (1) technical slide issues, (2) ML and image analysis aspects, (3) data challenges, and (4) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns or design choices in the computational implementation. To aid the adoption of ML for TIL assessment, we provide an in-depth discussion of ML and image analysis, including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial and routine clinical management of patients with triple-negative breast cancer. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Spatial analyses of immune cell infiltration in cancer: current methods and future directions: A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer

Modern histologic imaging platforms coupled with machine learning methods have provided new opportunities to map the spatial distribution of immune cells in the tumor microenvironment. However, there exists no standardized method for describing or analyzing spatial immune cell data, and most reported spatial analyses are rudimentary. In this review, we provide an overview of two approaches for reporting and analyzing spatial data (raster versus vector-based). We then provide a compendium of spatial immune cell metrics that have been reported in the literature, summarizing prognostic associations in the context of a variety of cancers. We conclude by discussing two well-described clinical biomarkers, the breast cancer stromal tumor infiltrating lymphocytes score and the colon cancer Immunoscore, and describe investigative opportunities to improve clinical utility of these spatial biomarkers. © 2023 The Pathological Society of Great Britain and Ireland.

Comparison of Programmed Cell Death Ligand 1 Status between Core Needle Biopsy and Surgical Specimens of Triple-Negative Breast Cancer

PURPOSE

Pembrolizumab is currently used to treat advanced triple-negative breast cancer (TNBC) and high-risk early TNBC with neoadjuvant chemotherapy (NAC). The tumor-infiltrating lymphocyte (TIL) level and programmed cell death ligand 1 (PD-L1) status are predictors of response to NAC and immune checkpoint inhibitor treatment. We aimed to investigate whether the PD-L1 status in core needle biopsies (CNBs) could represent the whole tumor in TNBC.

MATERIALS AND METHODS

A total of 49 patients diagnosed with TNBC who received upfront surgery without NAC between January 2018 and March 2021 were included. The PD-L1 expression (SP142 and 22C3 clones) and TIL were evaluated in paired CNBs and resected specimens. The concordance PD-L1 status and TIL levels between CNBs and resected specimens were analyzed.

RESULTS

PD-L1 positivity was more frequently observed in resected specimens. The overall reliability of TIL level in the CNB was good [intraclass correlation coefficient (ICC)=0.847, p<0.001]. The agreements of PD-L1 status were good and fair, respectively (SP142, κ=0.503, p<0.001; 22C3, κ=0.380, p=0.010). As the core number of CNB increased, the reliability and agreement also improved, especially from five tumor cores (TIL, ICC=0.911, p<0.001; PD-L1 [22C3], κ=0.750, p=0.028). Regarding PD-L1 (SP142), no further improvement was observed with ≥5 tumor cores (κ=0.600, p=0.058).

CONCLUSION

CNBs with ≥5 tumor cores were sufficient to represent the TIL level and PD-L1 (22C3) status in TNBC.

Immune Biomarkers in Triple-Negative Breast Cancer: Improving the Predictivity of Current Testing Methods

Triple-negative breast cancer (TNBC) poses a significant challenge in terms of prognosis and disease recurrence. The limited treatment options and the development of resistance to chemotherapy make it particularly difficult to manage these patients. However, recent research has been shifting its focus towards biomarker-based approaches for TNBC, with a particular emphasis on the tumor immune landscape. Immune biomarkers in TNBC are now a subject of great interest due to the presence of tumor-infiltrating lymphocytes (TILs) in these tumors. This characteristic often coincides with the presence of PD-L1 expression on both neoplastic cells and immune cells within the tumor microenvironment. Furthermore, a subset of TNBC harbor mismatch repair deficient (dMMR) TNBC, which is frequently accompanied by microsatellite instability (MSI). All of these immune biomarkers hold actionable potential for guiding patient selection in immunotherapy. To fully capitalize on these opportunities, the identification of additional or complementary biomarkers and the implementation of highly customized testing strategies are of paramount importance in TNBC. In this regard, this article aims to provide an overview of the current state of the art in immune-related biomarkers for TNBC. Specifically, it focuses on the various testing methodologies available and sheds light on the immediate future perspectives for patient selection. By delving into the advancements made in understanding the immune landscape of TNBC, this study aims to contribute to the growing body of knowledge in the field. The ultimate goal is to pave the way for the development of more personalized testing strategies, ultimately improving outcomes for TNBC patients.

Utility of Indigenously Developed Square Grid Method for Evaluation of Tumor-Stroma Ratio and Stromal Tumor-Infiltrating Lymphocytes in Invasive Breast Carcinoma: A Pilot Study

Background & Objective

Invasive breast carcinoma (IBC) is the most commonly diagnosed cancer among women in India. The conventional visual method of evaluation of Tumor-Stroma Ratio (TSR) and Stromal Tumor-Infiltrating Lymphocytes (sTIL) appears to be subjective. The present study aims to evaluate the utility of the indigenously designed square grid method for the evaluation of tumor-stroma ratio and stromal tumor-infiltrating lymphocytes in invasive breast carcinoma by assessing the inter-observer variability.

Methods

This was a retrospective study conducted at a rural tertiary care referral institute from July 2018 to June 2020. In each case, microphotographs were taken from 10 representative fields in H&E-stained sections for evaluating TSR in low-power and sTIL in high-power. Both the parameters were evaluated employing an indigenously designed square grid applied onto microphotographs in the power-point slides by making use of principles of the Pythagorean theorem. Both parameters were separately evaluated by two pathologists. Cohen kappa statistics was the statistical tool used to analyze inter-observer variability.

Results

Thirty cases were analyzed. Invasive breast carcinoma of no special type (IBC-NST) was the most common histopathological type (26 cases (86.67%)). For TRS evaluation, a Kappa value of 0.78 suggested substantial agreement with an agreement of 91.67%. For sTIL evaluation, a Kappa value of 0.51 suggested moderate agreement with an agreement of 88.33%. The P-values were statistically highly significant (P<0.001).

Conclusion

Square grid method is a novel technique for evaluating TSR and sTIL in invasive breast carcinoma. It can be considered an example of the application of Pythagoras' theorem in Pathology.

Automated Detection and Scoring of Tumor-Infiltrating Lymphocytes in Breast Cancer Histopathology Slides

Detection of tumor-infiltrating lymphocytes (TILs) in cancer images has gained significant importance as these lymphocytes can be used as a biomarker in cancer detection and treatment procedures. Our goal was to develop and apply a TILs detection tool that utilizes deep learning models, following two sequential steps. First, based on the guidelines from the International Immuno-Oncology Biomarker Working Group (IIOBWG) on Breast Cancer, we labeled 63 large pathology imaging slides and annotated the TILs in the stroma area to create the dataset required for model development. In the second step, various machine learning models were employed and trained to detect the stroma where U-Net deep learning structure was able to achieve 98% accuracy. After detecting the stroma area, a Mask R-CNN model was employed for the TILs detection task. The R-CNN model detected the TILs in various images and was used as the backbone analysis network for the GUI development of the TILs detection tool. This is the first study to combine two deep learning models for TILs detection at the cellular level in breast tumor histopathology slides. Our novel approach can be applied to scoring TILs in large cancer slides. Statistical analysis showed that the output of the implemented approach had 95% concordance with the scores assigned by the pathologists, with a p-value of 0.045 (n = 63). This demonstrated that the results from the developed software were statistically meaningful and highly accurate. The implemented approach in analyzing whole tumor histology slides and the newly developed TILs detection tool can be used for research purposes in biomedical and pathology applications and it can provide researchers and clinicians with the TIL score for various input images. Future research using additional breast cancer slides from various sources for further training and validation of the developed models is necessary for more inclusive, rigorous, and robust clinical applications.

A phase Ib trial of pembrolizumab plus paclitaxel or flat-dose capecitabine in 1st/2nd line metastatic triple-negative breast cancer

Chemoimmunotherapy with anti-programmed cell death 1/ligand 1 and cytotoxic chemotherapy is a promising therapeutic modality for women with triple-negative breast cancer, but questions remain regarding optimal chemotherapy backbone and biomarkers for patient selection. We report final outcomes from a phase Ib trial evaluating pembrolizumab (200 mg IV every 3 weeks) with either weekly paclitaxel (80 mg/m² weekly) or flat-dose capecitabine (2000 mg orally twice daily for 7 days of every 14-day cycle) in the 1st/2nd line setting. The primary endpoint is safety (receipt of 2 cycles without grade III/IV toxicities requiring discontinuation or ≥21-day delays). The secondary endpoint is efficacy (week 12 objective response). Exploratory aims are to characterize immunologic effects of treatment over time, and to evaluate novel biomarkers. The trial demonstrates that both regimens meet the pre-specified safety endpoint (paclitaxel: 87%; capecitabine: 100%). Objective response rate is 29% for pembrolizumab/paclitaxel (n = 4/13, 95% CI: 10-61%) and 43% for pembrolizumab/capecitabine (n = 6/14, 95% CI: 18-71%). Partial responses are observed in two subjects with chemo-refractory metaplastic carcinoma (both in capecitabine arm). Both regimens are associated with significant peripheral leukocyte contraction over time. Response is associated with clinical PD-L1 score, non-receipt of prior chemotherapy, and the H&E stromal tumor-infiltrating lymphocyte score, but also by a novel 27 gene IO score and spatial biomarkers (lymphocyte spatial skewness). In conclusion, pembrolizumab with paclitaxel or capecitabine is safe and clinically active. Both regimens are lymphodepleting, highlighting the competing immunostimulatory versus lymphotoxic effects of cytotoxic chemotherapy. Further exploration of the IO score and spatial TIL biomarkers is warranted. The clinical trial registration is NCT02734290.

Image analysis-based tumor infiltrating lymphocytes measurement predicts breast cancer pathologic complete response in SWOG S0800 neoadjuvant chemotherapy trial

We assessed the predictive value of an image analysis-based tumor-infiltrating lymphocytes (TILs) score for pathologic complete response (pCR) and event-free survival in breast cancer (BC). About 113 pretreatment samples were analyzed from patients with stage IIB-IIIC HER-2-negative BC randomized to neoadjuvant chemotherapy ± bevacizumab. TILs quantification was performed on full sections using QuPath open-source software with a convolutional neural network cell classifier (CNN11). We used easTILs% as a digital metric of TILs score defined as [sum of lymphocytes area (mm2)/stromal area(mm2)] × 100. Pathologist-read stromal TILs score (sTILs%) was determined following published guidelines. Mean pretreatment easTILs% was significantly higher in cases with pCR compared to residual disease (median 36.1 vs.14.8%, p < 0.001). We observed a strong positive correlation (r = 0.606, p < 0.0001) between easTILs% and sTILs%. The area under the prediction curve (AUC) was higher for easTILs% than sTILs%, 0.709 and 0.627, respectively. Image analysis-based TILs quantification is predictive of pCR in BC and had better response discrimination than pathologist-read sTILs%.

Narrative review on the role of immunotherapy in early triple negative breast cancer: unveiling opportunities and overcoming challenges

Background and Objective

Triple negative breast cancer (TNBC) represents a highly aggressive breast cancer subtype, historically managed with chemotherapy regimens predominantly involving anthracyclines and taxanes, yielding unfavorable prognoses. This review endeavors to offer a thorough examination of the present state of treatment strategies for early stage triple negative breast cancer (eTNBC), with a particular emphasis on immunotherapy modalities, combination therapies, predictive biomarkers, and ongoing clinical trials. The principal aim of this review is to meticulously assess the available literature, ascertain significant discoveries, and engage in discussions regarding their potential implications for future research endeavors, clinical applications, and policy formulation.

Methods

This review was conducted using PubMed and Google Scholar databases, with the latest update performed in March 2023. The search strategy was designed to ensure a comprehensive analysis of the literature, with a focus on recent advancements.

Key Content and Findings

We critically assess the current eTNBC treatment landscape, covering efficacy and limitations of monotherapy, combination therapies, and predictive biomarkers. We highlight promising results from recent trials, address controversies surrounding chemotherapy, and explore optimal approaches for adjuvant and neoadjuvant therapy (NAT). Insights into personalized treatment strategies, ongoing trials, and future perspectives are provided, advancing our understanding of therapeutic options for eTNBC.

Conclusions

Through a comprehensive analysis of the literature, this review highlights the potential of immunotherapy, particularly in combination with chemotherapy, as a promising approach for treating eTNBC. However, further research is warranted to optimize treatment strategies, refine patient selection criteria, and identify reliable biomarkers for predicting response to immune checkpoint inhibitors (ICIs). The findings of this review hold significant implications for future research, clinical practice, and policy-making, offering valuable insights into the current challenges and advancements in eTNBC treatment. Ultimately, this knowledge can contribute to improved patient outcomes, enhanced quality of life, and the development of more effective therapeutic approaches for eTNBC.

A pathomic approach for tumor-infiltrating lymphocytes classification on breast cancer digital pathology images

Background and objectives

The detection of tumor-infiltrating lymphocytes (TILs) could aid in the development of objective measures of the infiltration grade and can support decision-making in breast cancer (BC). However, manual quantification of TILs in BC histopathological whole slide images (WSI) is currently based on a visual assessment, thus resulting not standardized, not reproducible, and time-consuming for pathologists. In this work, a novel pathomic approach, aimed to apply high-throughput image feature extraction techniques to analyze the microscopic patterns in WSI, is proposed. In fact, pathomic features provide additional information concerning the underlying biological processes compared to the WSI visual interpretation, thus providing more easily interpretable and explainable results than the most frequently investigated Deep Learning based methods in the literature.

Methods

A dataset containing 1037 regions of interest with tissue compartments and TILs annotated on 195 TNBC and HER2+ BC hematoxylin and eosin (H&E)-stained WSI was used. After segmenting nuclei within tumor-associated stroma using a watershed-based approach, 71 pathomic features were extracted from each nucleus and reduced using a Spearman's correlation filter followed by a nonparametric Wilcoxon rank-sum test and least absolute shrinkage and selection operator. The relevant features were used to classify each candidate nucleus as either TILs or non-TILs using 5 multivariable machine learning classification models trained using 5-fold cross-validation (1) without resampling, (2) with the synthetic minority over-sampling technique and (3) with downsampling. The prediction performance of the models was assessed using ROC curves.

Results

21 features were selected, with most of them related to the well-known TILs properties of having regular shape, clearer margins, high peak intensity, more homogeneous enhancement and different textural pattern than other cells. The best performance was obtained by Random-Forest with ROC AUC of 0.86, regardless of resampling technique.

Conclusions

The presented approach holds promise for the classification of TILs in BC H&E-stained WSI and could provide support to pathologists for a reliable, rapid and interpretable clinical assessment of TILs in BC.

ONEST (Observers Needed to Evaluate Subjective Tests) Analysis of Stromal Tumour-Infiltrating Lymphocytes (sTILs) in Breast Cancer and Its Limitations

Tumour-infiltrating lymphocytes (TILs) reflect antitumour immunity. Their evaluation of histopathology specimens is influenced by several factors and is subject to issues of reproducibility. ONEST (Observers Needed to Evaluate Subjective Tests) helps in determining the number of observers that would be sufficient for the reliable estimation of inter-observer agreement of TIL categorisation. This has not been explored previously in relation to TILs. ONEST analyses, using an open-source software developed by the first author, were performed on TIL quantification in breast cancers taken from two previous studies. These were one reproducibility study involving 49 breast cancers, 23 in the first circulation and 14 pathologists in the second circulation, and one study involving 100 cases and 9 pathologists. In addition to the estimates of the number of observers required, other factors influencing the results of ONEST were examined. The analyses reveal that between six and nine observers (range 2-11) are most commonly needed to give a robust estimate of reproducibility. In addition, the number and experience of observers, the distribution of values around or away from the extremes, and outliers in the classification also influence the results. Due to the simplicity and the potentially relevant information it may give, we propose ONEST to be a part of new reproducibility analyses.

Histopathological subtyping of high-grade serous ovarian cancer using whole slide imaging

OBJECTIVE

We have established 4 histopathologic subtyping of high-grade serous ovarian cancer (HGSOC) and reported that the mesenchymal transition (MT) type has a worse prognosis than the other subtypes. In this study, we modified the histopathologic subtyping algorithm to achieve high interobserver agreement in whole slide imaging (WSI) and to characterize the tumor biology of MT type for treatment individualization.

METHODS

Four observers performed histopathological subtyping using WSI of HGSOC in The Cancer Genome Atlas data. As a validation set, cases from Kindai and Kyoto Universities were independently evaluated by the 4 observers to determine concordance rates. In addition, genes highly expressed in MT type were examined by gene ontology term analysis. Immunohistochemistry was also performed to validate the pathway analysis.

RESULTS

After algorithm modification, the kappa coefficient, which indicates interobserver agreement, was greater than 0.5 (moderate agreement) for the 4 classifications and greater than 0.7 (substantial agreement) for the 2 classifications (MT vs. non-MT). Gene expression analysis showed that gene ontology terms related to angiogenesis and immune response were enriched in the genes highly expressed in the MT type. CD31 positive microvessel density was higher in the MT type compared to the non-MT type, and tumor groups with high infiltration of CD8/CD103 positive immune cells were observed in the MT type.

CONCLUSION

We developed an algorithm for reproducible histopathologic subtyping classification of HGSOC using WSI. The results of this study may be useful for treatment individualization of HGSOC, including angiogenesis inhibitors and immunotherapy.

Predictive Biomarkers for Response to Immunotherapy in Triple Negative Breast Cancer: Promises and Challenges

Triple negative breast cancer (TNBC) is a highly heterogeneous disease with a poor prognosis and a paucity of therapeutic options. In recent years, immunotherapy has emerged as a new treatment option for patients with TNBC. However, this therapeutic evolution is paralleled by a growing need for biomarkers which allow for a better selection of patients who are most likely to benefit from this immune checkpoint inhibitor (ICI)-based regimen. These biomarkers will not only facilitate a better optimization of treatment strategies, but they will also avoid unnecessary side effects in non-responders, and limit the increasing financial toxicity linked to the use of these agents. Huge efforts have been deployed to identify predictive biomarkers for the ICI, but until now, the fruits of this labor remained largely unsatisfactory. Among clinically validated biomarkers, only programmed death-ligand 1 protein (PD-L1) expression has been prospectively assessed in TNBC trials. In addition to this, microsatellite instability and a high tumor mutational burden are approved as tumor agnostic biomarkers, but only a small percentage of TNBC fits this category. Furthermore, TNBC should no longer be approached as a single biological entity, but rather as a complex disease with different molecular, clinicopathological, and tumor microenvironment subgroups. This review provides an overview of the validated and evolving predictive biomarkers for a response to ICI in TNBC.

Tumor Infiltrating Lymphocytes across Breast Cancer Subtypes: Current Issues for Biomarker Assessment

Tumor-infiltrating lymphocytes (TILs) represent a surrogate biomarker of anti-tumor, lymphocyte-mediated immunity. In early, triple-negative breast cancer, TILs have level 1B of evidence to predict clinical outcomes. TILs represent a promising biomarker to select patients who can experience a better prognosis with de-intensified cancer treatments and derive larger benefits from immune checkpoint inhibitors. However, the assessment and the validation of TILs as a biomarker require a prospective and rigorous demonstration of its clinical validity and utility, provided reproducible analytical performance. With pending data about the prospective validation of TILs' clinical validity to modulate treatments in early breast cancer, this review summarizes the most important current issues and future challenges related to the implementation of TILs assessments across all breast cancer subtypes and their potential integration into clinical practice.

The Great Immune Escape: Understanding the Divergent Immune Response in Breast Cancer Subtypes

Breast cancer, the most common type of cancer affecting women, encompasses a collection of histologic (mainly ductal and lobular) and molecular subtypes exhibiting diverse clinical presentation, disease trajectories, treatment options, and outcomes. Immunotherapy has revolutionized treatment for some solid tumors but has shown limited promise for breast cancers. In this review, we summarize recent advances in our understanding of the complex interactions between tumor and immune cells in subtypes of breast cancer at the cellular and microenvironmental levels. We aim to provide a perspective on opportunities for future immunotherapy agents tailored to specific features of each subtype of breast cancer.

SIGNIFICANCE

Although there are currently over 200 ongoing clinical trials testing immunotherapeutics, such as immune-checkpoint blockade agents, these are largely restricted to the triple-negative and HER2+ subtypes and primarily focus on T cells. With the rapid expansion of new in vitro, in vivo, and clinical data, it is critical to identify and highlight the challenges and opportunities unique for each breast cancer subtype to drive the next generation of treatments that harness the immune system.

Association of Machine Learning-Based Assessment of Tumor-Infiltrating Lymphocytes on Standard Histologic Images With Outcomes of Immunotherapy in Patients With NSCLC

Importance

Currently, predictive biomarkers for response to immune checkpoint inhibitor (ICI) therapy in lung cancer are limited. Identifying such biomarkers would be useful to refine patient selection and guide precision therapy.

Objective

To develop a machine-learning (ML)-based tumor-infiltrating lymphocytes (TILs) scoring approach, and to evaluate TIL association with clinical outcomes in patients with advanced non-small cell lung cancer (NSCLC).

Design, Setting, and Participants

This multicenter retrospective discovery-validation cohort study included 685 ICI-treated patients with NSCLC with median follow-up of 38.1 and 43.3 months for the discovery (n = 446) and validation (n = 239) cohorts, respectively. Patients were treated between February 2014 and September 2021. We developed an ML automated method to count tumor, stroma, and TIL cells in whole-slide hematoxylin-eosin-stained images of NSCLC tumors. Tumor mutational burden (TMB) and programmed death ligand-1 (PD-L1) expression were assessed separately, and clinical response to ICI therapy was determined by medical record review. Data analysis was performed from June 2021 to April 2022.

Exposures

All patients received anti-PD-(L)1 monotherapy.

Main Outcomes and Measures

Objective response rate (ORR), progression-free survival (PFS), and overall survival (OS) were determined by blinded medical record review. The area under curve (AUC) of TIL levels, TMB, and PD-L1 in predicting ICI response were calculated using ORR.

Results

Overall, there were 248 (56%) women in the discovery cohort and 97 (41%) in the validation cohort. In a multivariable analysis, high TIL level (≥250 cells/mm2) was independently associated with ICI response in both the discovery (PFS: HR, 0.71; P = .006; OS: HR, 0.74; P = .03) and validation (PFS: HR = 0.80; P = .01; OS: HR = 0.75; P = .001) cohorts. Survival benefit was seen in both first- and subsequent-line ICI treatments in patients with NSCLC. In the discovery cohort, the combined models of TILs/PD-L1 or TMB/PD-L1 had additional specificity in differentiating ICI responders compared with PD-L1 alone. In the PD-L1 negative (<1%) subgroup, TIL levels had superior classification accuracy for ICI response (AUC = 0.77) compared with TMB (AUC = 0.65).

Conclusions and Relevance

In these cohorts, TIL levels were robustly and independently associated with response to ICI treatment. Patient TIL assessment is relatively easily incorporated into the workflow of pathology laboratories at minimal additional cost, and may enhance precision therapy.

Anti-TGF-β/PD-L1 bispecific antibody promotes T cell infiltration and exhibits enhanced antitumor activity in triple-negative breast cancer

BACKGROUND

Agents blocking programmed cell death protein 1/programmed death-ligand 1 (PD-1/PD-L1) have been approved for triple-negative breast cancer (TNBC). However, the response rate of anti-PD-1/PD-L1 is still unsatisfactory, partly due to immunosuppressive factors such as transforming growth factor-beta (TGF-β). In our previous pilot study, the bispecific antibody targeting TGF-β and murine PD-L1 (termed YM101) showed potent antitumor effect. In this work, we constructed a bispecific antibody targeting TGF-β and human PD-L1 (termed BiTP) and explored the antitumor effect of BiTP in TNBC.

METHODS

BiTP was developed using Check-BODY^TM bispecific platform. The binding affinity of BiTP was measured by surface plasmon resonance, ELISA, and flow cytometry. The bioactivity was assessed by Smad and NFAT luciferase reporter assays, immunofluorescence, western blotting, and superantigen stimulation assays. The antitumor activity of BiTP was explored in humanized epithelial-mesenchymal transition-6-hPDL1 and 4T1-hPDL1 murine TNBC models. Immunohistochemical staining, flow cytometry, and bulk RNA-seq were used to investigate the effect of BiTP on immune cell infiltration.

RESULTS

BiTP exhibited high binding affinity to dual targets. In vitro experiments verified that BiTP effectively counteracted TGF-β-Smad and PD-L1-PD-1-NFAT signaling. In vivo animal experiments demonstrated that BiTP had superior antitumor activity relative to anti-PD-L1 and anti-TGF-β monotherapy. Mechanistically, BiTP decreased collagen deposition, enhanced CD8⁺ T cell penetration, and increased tumor-infiltrating lymphocytes. This improved tumor microenvironment contributed to the potent antitumor activity of BiTP.

CONCLUSION

BiTP retains parent antibodies' binding affinity and bioactivity, with superior antitumor activity to parent antibodies in TNBC. Our data suggest that BiTP might be a promising agent for TNBC treatment.

Quantitative Assessment of Tumor-Infiltrating Lymphocytes Using Machine Learning Predicts Survival in Muscle-Invasive Bladder Cancer

(1) Purpose: Although assessment of tumor-infiltrating lymphocytes (TILs) has been acknowledged to have important predictive prognostic value in muscle-invasive bladder cancer (MIBC), it is limited by inter- and intra-observer variability, hampering widespread clinical application. We aimed to evaluate the prognostic value of quantitative TILs score based on a machine learning (ML) algorithm to identify MIBC patients who might benefit from immunotherapy or the de-escalation of therapy. (2) Methods: We constructed an artificial neural network classifier for tumor cells, lymphocytes, stromal cells, and “ignore” cells from hematoxylin-and-eosin-stained slide images using the QuPath open source software. We defined four unique TILs variables based on ML to analyze TILs measurements. Pathological slide images from 133 MIBC patients were retrospectively collected as the discovery set to determine the optimal association of ML-read TILs variables with patient survival outcomes. For validation, we evaluated an independent external validation set consisting of 247 MIBC patients. (3) Results: We found that all four TILs variables had significant prognostic associations with survival outcomes in MIBC patients (p < 0.001 for all comparisons), with higher TILs score being associated with better prognosis. Univariate and multivariate Cox regression analyses demonstrated that electronic TILs (eTILs) variables were independently associated with overall survival after adjustment for clinicopathological factors including age, sex, and pathological stage (p < 0.001 for all analyses). Results analyzed in different subgroups showed that the eTILs variable was a strong prognostic factor that was not redundant with pre-existing clinicopathological features (p < 0.05 for all analyses). (4) Conclusion: ML-driven cell classifier-defined TILs variables were robust and independent prognostic factors in two independent cohorts of MIBC patients. eTILs have the potential to identify a subset of high-risk stage II or stage III-IV MIBC patients who might benefit from adjuvant immunotherapy.

Artificial Intelligence-Powered Whole-Slide Image Analyzer Reveals a Distinctive Distribution of Tumor-Infiltrating Lymphocytes in Neuroendocrine Neoplasms

Despite the importance of tumor-infiltrating lymphocytes (TIL) and PD-L1 expression to the immune checkpoint inhibitor (ICI) response, a comprehensive assessment of these biomarkers has not yet been conducted in neuroendocrine neoplasm (NEN). We collected 218 NENs from multiple organs, including 190 low/intermediate-grade NENs and 28 high-grade NENs. TIL distribution was derived from Lunit SCOPE IO, an artificial intelligence (AI)-powered hematoxylin and eosin (H&E) analyzer, as developed from 17,849 whole slide images. The proportion of intra-tumoral TIL-high cases was significantly higher in high-grade NEN (75.0% vs. 46.3%, p = 0.008). The proportion of PD-L1 combined positive score (CPS) ≥ 1 case was higher in high-grade NEN (85.7% vs. 33.2%, p < 0.001). The PD-L1 CPS ≥ 1 group showed higher intra-tumoral, stromal, and combined TIL densities, compared to the CPS < 1 group (7.13 vs. 2.95, p < 0.001; 200.9 vs. 120.5, p < 0.001; 86.7 vs. 56.1, p = 0.004). A significant correlation was observed between TIL density and PD-L1 CPS (r = 0.37, p < 0.001 for intra-tumoral TIL; r = 0.24, p = 0.002 for stromal TIL and combined TIL). AI-powered TIL analysis reveals that intra-tumoral TIL density is significantly higher in high-grade NEN, and PD-L1 CPS has a positive correlation with TIL densities, thus showing its value as predictive biomarkers for ICI response in NEN.

Impact of stromal tumor-infiltrating lymphocytes (sTILs) on response to neoadjuvant chemotherapy in triple-negative early breast cancer in the WSG-ADAPT TN trial

BACKGROUND

Higher density of stromal tumor-infiltrating lymphocytes (sTILs) at baseline has been associated with increased rates of pathological complete response (pCR) after neoadjuvant chemotherapy (NACT) in triple-negative breast cancer (TNBC). While evidence supports favorable association of pCR with survival in TNBC, an independent impact of sTILs (after adjustment for pCR) on survival is not yet established. Moreover, the impact of sTIL dynamics during NACT on pCR and survival in TNBC is unknown.

METHODS

The randomized WSG-ADAPT TN phase II trial compared efficacy of 12-week nab-paclitaxel with gemcitabine versus carboplatin. This preplanned translational analysis assessed impacts of sTIL measurements at baseline (sTIL-0) and after 3 weeks of chemotherapy (sTIL-3) on pCR and invasive disease-free survival (iDFS). Predictive performance of sTIL-0 and sTIL-3 for pCR was quantified by ROC analysis and logistic regression; Kaplan-Meier estimation and Cox regression (with mediation analysis) were used to determine their impact on iDFS.

RESULTS

For prediction of pCR, the AUC statistics for sTIL-0 and sTIL-3 were 0.60 and 0.63, respectively, in all patients; AUC for sTIL-3 was higher in NP/G. The positive predictive value (PPV) of "lymphocyte-predominant" status (sTIL-0 ≥ 60%) at baseline was 59.3%, though only 13.0% of patients had this status. To predict non-pCR, the cut point sTIL-0 ≤ 10% yielded PPV = 69.5% while addressing 33.8% of patients. Higher sTIL levels (particularly at 3 weeks) were independently and favorably associated with better iDFS, even after adjusting for pCR. For example, the adjusted hazard ratio for 3-week sTILs ≥ 60% (vs. < 60%) was 0.48 [0.23-0.99]. Low cellularity in 3-week biopsies was the strongest individual predictor for pCR (in both therapy arms), but not for iDFS.

CONCLUSION

The independent impact of sTILs on iDFS suggests that favorable immune response can influence key tumor biological processes for long-term survival. The results suggest that the reliability of pCR following neoadjuvant therapy as a surrogate for survival could vary among subgroups in TNBC defined by immune response or other factors. Dynamic measurements of sTILs under NACT could support immune response-guided patient selection for individualized therapy approaches for both very low levels (more effective therapies) and very high levels (de-escalation concepts).

TRIAL REGISTRATION

Clinical trials No: NCT01815242, retrospectively registered January 25, 2013.

Multiplex immunohistochemistry and high-throughput image analysis for evaluation of spatial tumor immune cell markers in human breast cancer

BACKGROUND

The clinicopathological significance of spatial tumor-infiltrating lymphocytes (TILs) subpopulations is not well studied due to lack of high-throughput scalable methodology for studies with large human sample sizes.

OBJECTIVE

Establishing a cyclic fluorescent multiplex immunohistochemistry (mIHC/IF) method coupled with computer-assisted high-throughput quantitative analysis to evaluate associations of six TIL markers (CD3, CD8, CD20, CD56, FOXP3, and PD-L1) with clinicopathological factors of breast cancer.

METHODS

Our 5-plex mIHC/IF staining was shown to be reliable and highly sensitive for labeling three biomarkers per tissue section. Through repetitive cycles of 5-plex mIHC/IF staining, more than 12 biomarkers could be detected per single tissue section. Using open-source software CellProfiler, the measurement pipelines were successfully developed for high-throughput multiplex evaluation of intratumoral and stromal TILs.

RESULTS

In analyses of 188 breast cancer samples from the Nashville Breast Health Study, high-grade tumors showed significantly increased intratumoral CD3+CD8+ CTL density (P= 0.0008, false discovery rate (FDR) adjusted P= 0.0168) and intratumoral PD-L1 expression (P= 0.0061, FDR adjusted P= 0.0602) compared with low-grade tumors.

CONCLUSIONS

The high- and low-grade breast cancers exhibit differential immune responses which may have clinical significances. The multiplexed imaging quantification strategies established in this study are reliable, cost-efficient and applicable in regular laboratory settings for high-throughput tissue biomarker studies, especially retrospective and population-based studies using archived paraffin tissues.

Survival prediction in triple negative breast cancer using multiple instance learning of histopathological images

Computational pathology is a rapidly expanding area for research due to the current global transformation of histopathology through the adoption of digital workflows. Survival prediction of breast cancer patients is an important task that currently depends on histopathology assessment of cancer morphological features, immunohistochemical biomarker expression and patient clinical findings. To facilitate the manual process of survival risk prediction, we developed a computational pathology framework for survival prediction using digitally scanned haematoxylin and eosin-stained tissue microarray images of clinically aggressive triple negative breast cancer. Our results show that the model can produce an average concordance index of 0.616. Our model predictions are analysed for independent prognostic significance in univariate analysis (hazard ratio = 3.12, 95% confidence interval [1.69,5.75], p < 0.005) and multivariate analysis using clinicopathological data (hazard ratio = 2.68, 95% confidence interval [1.44,4.99], p < 0.005). Through qualitative analysis of heatmaps generated from our model, an expert pathologist is able to associate tissue features highlighted in the attention heatmaps of high-risk predictions with morphological features associated with more aggressive behaviour such as low levels of tumour infiltrating lymphocytes, stroma rich tissues and high-grade invasive carcinoma, providing explainability of our method for triple negative breast cancer.

Objective assessment of tumor infiltrating lymphocytes as a prognostic marker in melanoma using machine learning algorithms

BACKGROUND

The prognostic value of tumor-infiltrating lymphocytes (TILs) assessed by machine learning algorithms in melanoma patients has been previously demonstrated but has not been widely adopted in the clinic. We evaluated the prognostic value of objective automated electronic TILs (eTILs) quantification to define a subset of melanoma patients with a low risk of relapse after surgical treatment.

METHODS

We analyzed data for 785 patients from 5 independent cohorts from multiple institutions to validate our previous finding that automated TIL score is prognostic in clinically-localized primary melanoma patients. Using serial tissue sections of the Yale TMA-76 melanoma cohort, both immunofluorescence and Hematoxylin-and-Eosin (H&E) staining were performed to understand the molecular characteristics of each TIL phenotype and their associations with survival outcomes.

FINDINGS

Five previously-described TIL variables were each significantly associated with overall survival (p<0.0001). Assessing the receiver operating characteristic (ROC) curves by comparing the clinical impact of two models suggests that etTILs (electronic total TILs) (AUC: 0.793, specificity: 0.627, sensitivity: 0.938) outperformed eTILs (AUC: 0.77, specificity: 0.51, sensitivity: 0.938). We also found that the specific molecular subtype of cells representing TILs includes predominantly cells that are CD3+ and CD8+ or CD4+ T cells.

INTERPRETATION

eTIL% and etTILs scores are robust prognostic markers in patients with primary melanoma and may identify a subgroup of stage II patients at high risk of recurrence who may benefit from adjuvant therapy. We also show the molecular correlates behind these scores. Our data support the need for prospective testing of this algorithm in a clinical trial.

FUNDING

This work was also supported by a sponsored research agreements from Navigate Biopharma and NextCure and by grants from the NIH including the Yale SPORE in in Skin Cancer, P50 CA121974, the Yale SPORE in Lung Cancer, P50 CA196530, NYU SPORE in Skin Cancer P50CA225450 and the Yale Cancer Center Support Grant, P30CA016359.

The Significance of External Quality Assessment Schemes for Molecular Testing in Clinical Laboratories

Simple Summary

Patients and clinicians often rely on the outcome of laboratory tests, but can we really trust these test results? Good quality management is key for laboratories to guarantee reliable test results. This review focusses on external quality assessment (EQA) schemes which are a tool for laboratories to examine and improve the quality of their testing routines. In this review, an overview of the role and importance of EQA schemes for clinical laboratories is given, and different types of EQA schemes and EQA providers available on the market are discussed, as well as recent developments in the EQA landscape.

Abstract

External quality assessment (EQA) schemes are a tool for clinical laboratories to evaluate and manage the quality of laboratory practice with the support of an independent party (i.e., an EQA provider). Depending on the context, there are different types of EQA schemes available, as well as various EQA providers, each with its own field of expertise. In this review, an overview of the general requirements for EQA schemes and EQA providers based on international guidelines is provided. The clinical and scientific value of these kinds of schemes for clinical laboratories, clinicians and patients are highlighted, in addition to the support EQA can provide to other types of laboratories, e.g., laboratories affiliated to biotech companies. Finally, recent developments and challenges in laboratory medicine and quality management, for example, the introduction of artificial intelligence in the laboratory and the shift to a more individual-approach instead of a laboratory-focused approach, are discussed. EQA schemes should represent current laboratory practice as much as possible, which poses the need for EQA providers to introduce latest laboratory innovations in their schemes and to apply up-to-date guidelines. By incorporating these state-of-the-art techniques, EQA aims to contribute to continuous learning.

Computational portraits of the tumoral microenvironment in human breast cancer

Breast cancer is the most diagnosed cancer in humans. In recent years, myxoid and proportionated stroma have been described as clinically significant in many cancer subtypes. Here computational portraits of tumor-associated stromata were created from a machine learning (ML) classifier using QuPath to evaluate proportionated stromal area (PSA), myxoid stromal ratio (MSR), and immune stroma proportion (ISP) from whole slide images (WSI). The ML classifier was validated in independent training (n = 40) and validation (n = 109) cohorts finding MSR, PSA, and ISP to be associated with tumor stage, lymph node status, Nottingham grade, stromal differentiation (SD), tumor size, estrogen receptor (ER), progesterone receptor (PR), and receptor tyrosine-protein kinase erbB-2 (HER-2). Overall, MSR correlated better with the clinicopathologic profile than PSA and ISP. High MSR was found to be associated with high tumor stage, low ISP, and high Nottingham histologic score. As a computational biomarker, high MSR was more likely to be associated with luminal B like, Her-2 enriched, and triple-negative biomarker status when compared to luminal A like. The supervised ML superpixel approach demonstrated here can be performed by a trained pathologist to provide a faster and more uniformed approach to the analysis to the tumoral microenvironment (TME). The TME may be relevant for clinical decision-making, determining chemotherapeutic efficacy, and guiding a more overall precision-based breast cancer care.