Unveiling the Power of Model-Agnostic Multiscale Analysis for Enhancing Artificial Intelligence Models in Breast Cancer Histopathology Images.

Developing AI models for digital pathology has traditionally relied on single-scale analysis of histopathology slides. However, a whole slide image is a rich digital representation of the tissue, captured at various magnification levels. Limiting our analysis to a single scale overlooks critical information, spanning from intricate high-resolution cellular details to broad low-resolution tissue structures. In this study, we propose a model-agnostic multiresolution feature aggregation framework tailored for the analysis of histopathology slides in the context of breast cancer, on a multicohort dataset of 2038 patient samples. We have adapted 9 state-of-the-art multiple instance learning models on our multi-scale methodology and evaluated their performance on grade prediction, TP53 mutation status prediction and survival prediction. The results prove the dominance of the multiresolution methodology, and specifically, concatenating or linearly transforming via a learnable layer the feature vectors of image patches from a high (20x) and low (10x) magnification factors achieve improved performance for all prediction tasks across domain-specific and imagenet-based features. On the contrary, the performance of uniresolution baseline models was not consistent across domain-specific and imagenet-based features. Moreover, we shed light on the inherent inconsistencies observed in models trained on whole-tissue-sections when validated against biopsy-based datasets. Despite these challenges, our findings underscore the superiority of multiresolution analysis over uniresolution methods. Finally, cross-scale analysis also benefits the explainability aspects of attention-based architectures, since one can extract attention maps at the tissue- and cell-levels, improving the interpretation of the model's decision. The code and results of this study can be found at github.com/tsikup/multiresolution_histopathology.

Longitudinal molecular profiling elucidates immunometabolism dynamics in breast cancer.

Although metabolic reprogramming within tumor cells and tumor microenvironment (TME) is well described in breast cancer, little is known about how the interplay of immune state and cancer metabolism evolves during treatment. Here, we characterize the immunometabolic profiles of tumor tissue samples longitudinally collected from individuals with breast cancer before, during and after neoadjuvant chemotherapy (NAC) using proteomics, genomics and histopathology. We show that the pre-, on-treatment and dynamic changes of the immune state, tumor metabolic proteins and tumor cell gene expression profiling-based metabolic phenotype are associated with treatment response. Single-cell/nucleus RNA sequencing revealed distinct tumor and immune cell states in metabolism between cold and hot tumors. Potential drivers of NAC based on above analyses were validated in vitro. In summary, the study shows that the interaction of tumor-intrinsic metabolic states and TME is associated with treatment outcome, supporting the concept of targeting tumor metabolism for immunoregulation.

The role of serum thymidine kinase 1 activity in neoadjuvant-treated HER2-positive breast cancer: biomarker analysis from the Swedish phase II randomized PREDIX HER2 trial.

BACKGROUND: Thymidine kinase 1 (TK1) plays a pivotal role in DNA synthesis and cellular proliferation. TK1 has been studied as a prognostic marker and as an early indicator of treatment response in human epidermal growth factor 2 (HER2)-negative early and metastatic breast cancer (BC). However, the prognostic and predictive value of serial TK1 activity in HER2-positive BC remains unknown. METHODS: In the PREDIX HER2 trial, 197 HER2-positive BC patients were randomized to neoadjuvant trastuzumab, pertuzumab, and docetaxel (DPH) or trastuzumab emtansine (T-DM1), followed by surgery and adjuvant epirubicin and cyclophosphamide. Serum samples were prospectively collected from all participants at multiple timepoints: at baseline, after cycle 1, 2, 4, and 6, at end of adjuvant therapy, annually for a total period of 5 years and/or at the time of recurrence. The associations of sTK1 activity with baseline characteristics, pathologic complete response (pCR), event-free survival (EFS), and disease-free survival (DFS) were evaluated. RESULTS: No association was detected between baseline sTK1 levels and all the baseline clinicopathologic characteristics. An increase of TK1 activity from baseline to cycle 2 was seen in all cases. sTK1 level at baseline, after 2 and 4 cycles was not associated with pCR status. After a median follow-up of 58 months, 23 patients had EFS events. There was no significant effect between baseline or cycle 2 sTK1 activity and time to event. A non-significant trend was noted among patents with residual disease (non-pCR) and high sTK1 activity at the end of treatment visit, indicating a potentially worse long-term prognosis. CONCLUSION: sTK1 activity increased following neoadjuvant therapy for HER2-positive BC but was not associated with patient outcomes or treatment benefit. However, the post-surgery prognostic value in patients that have not attained pCR warrants further investigation. TRIAL REGISTRATION: ClinicalTrials.gov, NCT02568839. Registered on 6 October 2015.

Multiresolution Self-Supervised Feature Integration via Attention Multiple Instance Learning for Histopathology Analysis.

Digital histopathology image analysis of tumor tissue sections has seen great research interest for automating standard diagnostic tasks, but also for developing novel prognostic biomarkers. However, research has mainly been focused on developing uniresolution models, capturing either high-resolution cellular features or low-resolution tissue architectural features. In addition, in the patch-based weakly-supervised training of deep learning models, the features which represent the intratumoral heterogeneity are lost. In this study, we propose a multiresolution attention-based multiple instance learning framework that can capture cellular and contextual features from the whole tissue for predicting patient-level outcomes. Several basic mathematical operations were examined for integrating multiresolution features, i.e. addition, mean, multiplication and concatenation. The proposed multiplication-based multiresolution model performed the best (AUC=0.864), while all multiresolution models outperformed the uniresolution baseline models (AUC=0.669, 0.713) for breast-cancer grading. (Implementation: https://github.com/tsikup/multiresolution-clam).

The ever-expanding landscape of antibody-drug conjugates (ADCs) in solid tumors: A systematic review.

BACKGROUND: The advent of targeted therapies signaled novel avenues for more optimal oncological outcomes. Antibody-drug conjugates (ADCs) have risen as a cornerstone of the ever-expanding targeted therapy era. The purpose of this systematic review is to delineate the rapidly evolving clinical landscape of ADCs for solid tumors. METHODS: A literature search was performed in Medline, Embase and Cochrane databases for phase II and III clinical trials. Outcomes of interest were the objective response rate, overall survival, progression-free survival and adverse events. RESULTS: A total of 92 clinical trials (76 phase II and 16 phase III) evaluated the efficacy and safety of ADCs for a plethora of solid tumors. Out of the 30 investigated ADCs, 8 have received approval by regulatory organizations for solid tumors. Currently, 52 phase III clinical trials for ADCs are ongoing. CONCLUSION: ADCs have shown promising results for several solid tumors and various cancer settings.

Discordance of PIK3CA mutational status between primary and metastatic breast cancer: a systematic review and meta-analysis.

INTRODUCTION: In light of the clinically meaningful results of the PI3K inhibitors in PIK3CA-mutated metastatic breast cancer (BC) patients, the reliable identification of PIK3CA mutations is of outmost importance. However, lack of evidence on the optimal site and timing of assessment, presence of temporal heterogeneity and analytical factors pose several challenges in clinical routine. We aimed to study the discordance rates of PIK3CA mutational status between primary and matched metastatic tumors. METHODS: A systematic literature search was performed in three different databases (Embase, Pubmed, Web of Science) and-upon screening-a total of 25 studies reporting PIK3CA mutational status both on primary breast tumors and their matched metastases were included in this meta-analysis. The random-effects model was used for pooled analyses of discordance of PIK3CA mutational status. RESULTS: The overall discordance rate of PIK3CA mutational status was 9.8% (95% CI, 7.0-13.0; n = 1425) and did not significantly differ within BC subtypes or metastatic sites. The change was bi-directional, more commonly observed from PIK3CA mutated to wild-type status (14.9%, 95% CI 11.8-18.2; n tumor pairs = 453) rather than the opposite direction (8.9%, 95% CI 6.1-12.1; n tumor pairs = 943). CONCLUSIONS: Our results indicate the need of obtaining metastatic biopsies for PIK3CA-mutation analysis and the possibility of testing of the primary tumor, in case a re-biopsy deemed non-feasible.

The interplay between eosinophils and T cells in breast cancer immunotherapy.

Treatment with immune checkpoint inhibitors (ICI) has revolutionized cancer management for multiple tumor types, including breast cancer. However, not all patients respond to ICI, and unraveling the determinants and mechanisms of response still remains an unmet need. A recent study has uncovered the critical role of eosinophils in mediating immunotherapy effect in breast cancer, mainly by stimulating the activation of CD8+ T-cells. Furthermore, the intratumoral eosinophil recruitment was directed by CD4+ T cells and the interleukins IL-5 and IL-33, thus providing the rationale for targeting eosinophils to enhance ICI response.

Survival Outcomes, Digital TILs, and On-treatment PET/CT During Neoadjuvant Therapy for HER2-positive Breast Cancer: Results from the Randomized PREDIX HER2 Trial.

PURPOSE: PREDIX HER2 is a randomized Phase II trial that compared neoadjuvant docetaxel, trastuzumab, and pertuzumab (THP) with trastuzumab emtansine (T-DM1) for HER2-positive breast cancer. Rates of pathologic complete response (pCR) did not differ between the two groups. Here, we present the survival outcomes from PREDIX HER2 and investigate metabolic response and tumor-infiltrating lymphocytes (TIL) as prognostic factors. PATIENTS AND METHODS: In total, 202 patients with HER2-positive breast cancer were enrolled and 197 patients received six cycles of either THP or T-DM1. Secondary endpoints included event-free survival (EFS), recurrence-free survival (RFS), and overall survival (OS). Assessment with PET/CT was performed at baseline, after two and six treatment cycles. TILs were assessed manually at baseline biopsies, while image-based evaluation of TILs [digital TILs (DTIL)] was performed in digitized full-face sections. RESULTS: After a median follow-up of 5.21 years, there was no difference between the two treatment groups in terms of EFS [HR = 1.26; 95% confidence interval (CI), 0.54-2.91], RFS (HR = 0.69; 95% CI, 0.24-1.93), or OS (HR = 0.52; 95% CI, 0.09-2.82). Higher SUVmax at cycle 2 (C2) predicted lower pCR (ORadj = 0.65; 95% CI, 0.48-0.87; P = 0.005) and worse EFS (HRadj = 1.27; 95% CI, 1.12-1.41; P < 0.001). Baseline TILs and DTILs provided additional prognostic information to clinical parameters and C2 SUVmax. CONCLUSIONS: Long-term outcomes following neoadjuvant T-DM1 were similar to neoadjuvant THP. SUVmax after two cycles of neoadjuvant therapy for HER2-positive breast cancer may be an independent predictor of both short- and long-term outcomes. Combined assessment with TILs may facilitate early selection of poor responders for alternative treatment strategies.

Expression patterns and prognostic implications of tumor-infiltrating lymphocytes dynamics in early breast cancer patients receiving neoadjuvant therapy: A systematic review and meta-analysis.

Purpose: High levels of tumor-infiltrating lymphocytes (TILs) are associated with better outcomes in early breast cancer and higher pathological response rates to neoadjuvant chemotherapy especially in the triple-negative (TNBC) and HER2+ subtypes. However, the dynamic changes in TILs levels after neoadjuvant treatment (NAT) are less studied. This systematic review and meta-analysis aimed to investigate the patterns and role of TILs dynamics change in early breast cancer patients receiving NAT. Methods: Medline, Embase, Web of Science Core Collection and PubMed Central databases were searched for eligible studies. Data were extracted independently by two researchers and discordances were resolved by a third. Pooled TILs rates pre- & post-treatment (overall and per subtype), pooled rates of ΔTILs and direction of change after NAT as well as correlation of ΔTILs with survival outcomes were generated in the outcome analysis. Results: Of 2116 identified entries, 34 studies fulfilled the criteria and provided adequate data for the outcomes of interest. A decreased level of TILs was observed after NAT in paired samples across all subtypes. The effect of NAT on TILs was most prominent in TNBC subtype with a substantial change, either increase or decrease, in 79.3% (95% CI 61.7-92.6%) of the patients as well as in HER2+ disease (14.4% increased vs 46.2% decreased). An increase in ΔTILs in TNBC was associated with better disease-free/relapse-free survival in pooled analysis (univariate HR = 0.59, 95% CI: 0.37-0.95, p = 0.03). Conclusion: This meta-analysis illustrates the TILs dynamics during NAT for breast cancer and indicates prognostic implications of ΔTILs in TNBC. The potential clinical utility of the longitudinal assessment of TILs during neoadjuvant therapy warrants further validation.

Dissecting Tumor-Immune Microenvironment in Breast Cancer at a Spatial and Multiplex Resolution.

The tumor immune microenvironment (TIME) is an important player in breast cancer pathophysiology. Surrogates for antitumor immune response have been explored as predictive biomarkers to immunotherapy, though with several limitations. Immunohistochemistry for programmed death ligand 1 suffers from analytical problems, immune signatures are devoid of spatial information and histopathological evaluation of tumor infiltrating lymphocytes exhibits interobserver variability. Towards improved understanding of the complex interactions in TIME, several emerging multiplex in situ methods are being developed and gaining much attention for protein detection. They enable the simultaneous evaluation of multiple targets in situ, detection of cell densities/subpopulations as well as estimations of functional states of immune infiltrate. Furthermore, they can characterize spatial organization of TIME-by cell-to-cell interaction analyses and the evaluation of distribution within different regions of interest and tissue compartments-while digital imaging and image analysis software allow for reproducibility of the various assays. In this review, we aim to provide an overview of the different multiplex in situ methods used in cancer research with special focus on breast cancer TIME at the neoadjuvant, adjuvant and metastatic setting. Spatial heterogeneity of TIME and importance of longitudinal evaluation of TIME changes under the pressure of therapy and metastatic progression are also addressed.

Interplay between copy number alterations and immune profiles in the early breast cancer Scandinavian Breast Group 2004-1 randomized phase II trial: results from a feasibility study.

Emerging data indicate that genomic alterations can shape immune cell composition in early breast cancer. However, there is a need for complementary imaging and sequencing methods for the quantitative assessment of combined somatic copy number alteration (SCNA) and immune profiling in pathological samples. Here, we tested the feasibility of three approaches-CUTseq, for high-throughput low-input SCNA profiling, multiplexed fluorescent immunohistochemistry (mfIHC) and digital-image analysis (DIA) for quantitative immuno-profiling- in archival formalin-fixed paraffin-embedded (FFPE) tissue samples from patients enrolled in the randomized SBG-2004-1 phase II trial. CUTseq was able to reproducibly identify amplification and deletion events with a resolution of 100 kb using only 6 ng of DNA extracted from FFPE tissue and pooling together 77 samples into the same sequencing library. In the same samples, mfIHC revealed that CD4 + T-cells and CD68 + macrophages were the most abundant immune cells and they mostly expressed PD-L1 and PD-1. Combined analysis showed that the SCNA burden was inversely associated with lymphocytic infiltration. Our results set the basis for further applications of CUTseq, mfIHC and DIA to larger cohorts of early breast cancer patients.

Discordance of PD-L1 Expression at the Protein and RNA Levels in Early Breast Cancer.

We aimed to assess if the discrepant prognostic information between Programmed Death Ligand 1 (PD-L1) protein versus mRNA expression in early breast cancer (BC) could be attributed to heterogeneity in its expression. PD-L1 protein and mRNA expression in BC tissue microarrays from two clinical patient cohorts were evaluated (105 patients; cohort 1: untreated; cohort 2: neoadjuvant chemotherapy-treated). Immunohistochemistry (IHC) with SP142, SP263 was performed. PD-L1 mRNA was evaluated using bulk gene expression and RNA-FISH RNAscope®, the latter scored in a semi-quantitative manner and combined with immunofluorescence (IF) staining for the simultaneous detection of PD-L1 protein expression. PD-L1 expression was assessed in cores as a whole and in two regions of interest (ROI) from the same core. The cell origin of PD-L1 expression was evaluated using multiplex fluorescent IHC. IHC PD-L1 expression between SP142 and SP263 was concordant in 86.7% of cores (p < 0.001). PD-L1 IF/IHC was weakly correlated with spatial mRNA expression (concordance 54.6-71.2%). PD-L1 was mostly expressed by lymphocytes intra-tumorally, while its stromal expression was mostly observed in macrophages. Our results demonstrate only moderate concordance between the various methods of assessing PD-L1 expression at the protein and mRNA levels, which may be attributed to both analytical performance and spatial heterogeneity.

Discordance of PD-L1 status between primary and metastatic breast cancer: A systematic review and meta-analysis.

INTRODUCTION: Programmed cell death ligand 1 (PD-L1) expression is predictive for benefit from immunotherapy in several human malignancies including triple negative breast cancer. Lower positivity rates but a larger relative benefit from atezolizumab has been implied when PD-L1 status is assessed at metastatic sites. We aimed to study the discordance of PD-L1 expression between primary tumor and metastasis in breast cancer due to its potential clinical utility. METHODS: Cochrane Library, Embase, Medline and Web of science were searched for studies reporting on PD-L1 expression in primary and metastatic breast cancer, followed by data extraction. Outcomes included pooled PD-L1 positivity rates in tumor cells, immune cells or both in primary tumor and metastasis, PD-L1 discordance between matched primary tumors and metastasis and direction of discordance. RESULTS: Of 2552 identified entries following de-duplication, 20 studies fulfilled the predefined inclusion criteria. Pooled PD-L1 positivity rate was higher in primary tumors compared to metastasis when assessed in immune cells (51.2% vs 37.1% p < 0.001) and tumor/immune cells (30.1% vs 14.6% p < 0.001), but not in tumor cells (18.7% vs 17.8% p = 0.65). PD-L1 positivity was lowest when assessed in bone metastases (12%) and highest in lymph nodes (60%). Discordance between primary tumors and metastasis was bidirectional, with higher pooled discordance rates when PD-L1 expression was assessed in immune compared to tumor cells (39.5% vs 13.6%, p < 0.001). CONCLUSION: The observed considerable discordance between PD-L1 status in primary and metastatic breast cancer emphasizes the importance of appropriate tissue sampling when selecting patients for immunotherapy.

Neoadjuvant Trastuzumab, Pertuzumab, and Docetaxel vs Trastuzumab Emtansine in Patients With ERBB2-Positive Breast Cancer: A Phase 2 Randomized Clinical Trial.

Importance: Trastuzumab emtansine (T-DM1) is presently approved for treatment of advanced breast cancer and after incomplete response to neoadjuvant therapy, but the potential of T-DM1 as monotherapy is so far unknown. Objective: To assess pathologic complete response (pCR) to standard neoadjuvant therapy of combination docetaxel, trastuzumab, and pertuzumab (DTP) vs T-DM1 monotherapy in patients with ERBB2 (formerly HER2)-positive breast cancer. Design, Setting, and Participants: This randomized phase 2 trial, conducted at 9 sites in Sweden, enrolled 202 patients between December 1, 2014, and October 31, 2018. Participants were 18 years or older, with ERBB2-positive tumors larger than 20 mm and/or verified lymph node metastases. Analysis was performed on an intention-to-treat basis. Interventions: Patients were randomized to receive 6 cycles of DTP (standard group) or T-DM1 (investigational group). Crossover was recommended at lack of response or occurrence of intolerable toxic effects. Assessment with fluorine 18-labeled fluorodeoxyglucose (18F-FDG) positron emission tomography combined with computed tomography (PET-CT) was performed at baseline and after 2 and 6 treatment cycles. Main Outcome and Measures: Pathologic complete response, defined as ypT0 or Tis ypN0. Secondary end points were clinical and radiologic objective response; event-free survival, invasive disease-free survival, distant disease-free survival, and overall survival; safety; health-related quality of life (HRQoL); functional and biological tumor characteristics; and frequency of breast-conserving surgery. Results: Overall, 202 patients were randomized; 197 (99 women in the standard group [median age, 51 years (range, 26-73 years)] and 98 women in the investigational group [median age, 53 years (range, 28-74 years)]) were evaluable for the primary end point. Pathologic complete response was achieved in 45 patients in the standard group (45.5%; 95% CI 35.4%-55.8%) and 43 patients in the investigational group (43.9%; 95% CI 33.9%-54.3%). The difference was not statistically significant (P = .82). In a subgroup analysis, the pCR rate was higher in hormone receptor-negative tumors than in hormone receptor-positive tumors in both treatment groups (45 of 72 [62.5%] vs 45 of 125 [36.0%]). Three patients in the T-DM1 group experienced progression during therapy. In an exploratory analysis, tumor-infiltrating lymphocytes at 10% or more (median) estimated pCR significantly (odds ratio, 2.76; 95% CI, 1.42-5.36; P = .003). Response evaluation with 18F-FDG PET-CT revealed a relative decrease of maximum standardized uptake value by equal to or greater than 68.7% (median) was associated with pCR (odds ratio, 6.74, 95% CI, 2.75-16.51; P < .001). Conclusions and Relevance: In this study, treatment with standard neoadjuvant combination DTP was equal to T-DM1. Trial Registrations: ClinicalTrials.gov Identifier: NCT02568839; EudraCT number: 2014-000808-10.

PD-1 protein and gene expression as prognostic factors in early breast cancer.

BACKGROUND: There is a paucity of data on the prognostic value of programmed cell death protein 1 (PD-1) protein and gene expression in early breast cancer (BC) and the present study's aim was to comprehensively investigate it. METHODS: The study consisted of three parts: a correlative analysis of PD-1 protein and gene expression from an original patient cohort of 564 patients with early BC; a systematic review and trial-level meta-analysis on the association between PD-1 protein expression and disease-free survival/overall survival (OS) in early BC; and a pooled gene expression analysis from publicly available transcriptomic datasets regarding PDCD1 expression. RESULTS: In the study cohort, PD-1 protein, but not gene expression, was associated with improved OS (HRadj=0.73, 95% CI 0.55 to 0.97, p=0.027 and HRadj=0.88, 95% CI 0.68 to 1.13, p=0.312, respectively). In the trial-level meta-analysis, PD-1 protein expression was not found to be statistically significantly associated with outcomes in the overall population. Finally, in the pooled gene expression analysis, higher PDCD1 expression was associated with better OS in multivariable analysis in the entire population (HRadj=0.89, 95% CI 0.80 to 0.99, p=0.025) and in basal-like tumours. CONCLUSIONS: PD-1 protein and gene expression seem to be promising prognostic factors in early BC. Standardisation of detection and assessment methods is of utmost importance.

Programmed death-ligand 1 gene expression is a prognostic marker in early breast cancer and provides additional prognostic value to 21-gene and 70-gene signatures in estrogen receptor-positive disease.

Gene and protein expression of programmed death-ligand 1 (PD-L1) are prognostic in early breast cancer (BC), but their prognostic information is inconsistent at least in some biological subgroups. The validated prognostic gene signatures (GS) in BC are mainly based on proliferation and estrogen receptor (ER)-related genes. Here, we aimed to explore the prognostic capacity of PD-L1 expression at the protein vs mRNA levels and to investigate the prognostic information that PD-L1 can potentially add to routinely used GS. Gene expression data were derived from two early BC cohorts (cohort 1: 562 patients; cohort 2: 1081 patients). Tissue microarrays from cohort 1 were immunohistochemically (IHC) stained for PD-L1 using the SP263 clone. GS scores (21-gene, 70-gene) were calculated, and likelihood-ratio (LR) tests and concordance indices were used to evaluate the additional prognostic information for each signature. The immune cell composition was also evaluated using the CIBERSORT in silico tool. PD-L1 gene and protein expressions were independently associated with better prognosis. In ER+/HER2- patients, PD-L1 gene expression provided significant additional prognostic information beyond that of both 21-GS [LR-Δχ2  = 15.289 and LR-Δχ2  = 8.812, P < 0.01 for distant metastasis-free interval (DMFI) in cohorts 1 and 2, respectively] and 70-GS score alone (LR-Δχ2  = 18.198 and LR-Δχ2  = 8.467, P < 0.01 for DMFI in cohorts 1 and 2, respectively). PD-L1 expression was correlated with IHC-determined CD3+ cells (r = 0.41, P < 0.001) and with CD8+ (r = 0.62, P < 0.001) and CD4+ memory activated (r = 0.66, P < 0.001) but not with memory resting (r = -0.063, P = 0.14) or regulatory (r = -0.12, P < 0.01) T cells in silico. PD-L1 gene expression represents a promising favorable prognostic marker and can provide additional prognostic value to 21- and 70-gene scores in ER+/HER2- BC.

SETD2 mutation in renal clear cell carcinoma suppress autophagy via regulation of ATG12.

Inactivating mutations in the SETD2 gene, encoding for a nonredundant histone H3 methyltransferase and regulator of transcription, is a frequent molecular feature in clear cell renal cell carcinomas (ccRCC). SETD2 deficiency is associated with recurrence of ccRCC and bears low prognostic values. Targeting autophagy, a conserved catabolic process with critical functions in maintenance of cellular homeostasis and cell conservation under stress condition, is emerging as a potential therapeutic strategy to combat ccRCC. Epigenetics-based pathways are now appreciated as key components in the regulation of autophagy. However, whether loss of function in the SETD2 histone modifying enzyme occurring in ccRCC cells may impact on their ability to undergo autophagy remained to be explored. Here, we report that SETD2 deficiency in RCC cells is associated with the aberrant accumulation of both free ATG12 and of an additional ATG12-containing complex, distinct from the ATG5-ATG12 complex. Rescue of SETD2 functions in the SETD2 deficiency in RCC cells, or reduction of SETD2 expression level in RCC cells wild type for this enzyme, demonstrates that SETD2 deficiency in RCC is directly involved in the acquisition of these alterations in the autophagic process. Furthermore, we revealed that deficiency in SETD2, known regulator of alternative splicing, is associated with increased expression of a short ATG12 spliced isoform at the depend of the canonical long ATG12 isoform in RCC cells. The defect in the ATG12-dependent conjugation system was found to be associated with a decrease autophagic flux, in accord with the role for this ubiquitin-like protein conjugation system in autophagosome formation and expansion. Finally, we report that SETD2 and ATG12 gene expression levels are associated with favorable respective unfavorable prognosis in ccRCC patients. Collectively, our findings bring further argument for considering the SETD2 gene status of ccRCC tumors, when therapeutic interventions, such as targeting the autophagic process, are considered to combat these kidney cancers.

STAT3 Activity Promotes Programmed-Death Ligand 1 Expression and Suppresses Immune Responses in Breast Cancer.

Signal transducer and activator of transcription 3 (STAT3) is an oncogene and multifaceted transcription factor involved in multiple cellular functions. Its role in modifying anti-tumor immunity has been recently recognized. In this study, the biologic effects of STAT3 on immune checkpoint expression and anti-tumor responses were investigated in breast cancer (BC). A transcriptional signature of phosphorylated STAT3 was positively correlated with PD-L1 expression in two independent cohorts of early BC. Pharmacologic inhibition and gene silencing of STAT3 led to decreased Programmed Death Ligand 1 (PD-L1) expression levels in vitro, and resulted as well in reduction of tumor growth and decreased metastatic dissemination in a mammary carcinoma mouse model. The hampering of tumor progression was correlated to an anti-tumoral macrophage phenotype and accumulation of natural-killer cells, but also in reduced accrual of cytotoxic lymphocytes. In human BC, pro-tumoral macrophages correlated to PD-L1 expression, proliferation status and higher grade of malignancy, indicating a subset of patients with immunosuppressive properties. In conclusion, this study provides evidence for STAT3-mediated regulation of PD-L1 and modulation of immune microenvironment in BC.

Prognostic Implications of PD-L1 Expression in Breast Cancer: Systematic Review and Meta-analysis of Immunohistochemistry and Pooled Analysis of Transcriptomic Data.

PURPOSE: Conflicting data have been reported on the prognostic value of PD-L1 protein and gene expression in breast cancer.Experimental Design: Medline, Embase, Cochrane Library, and Web of Science Core Collection were searched, and data were extracted independently by two researchers. Outcomes included pooled PD-L1 protein positivity in tumor cells, immune cells, or both, per subtype and per antibody used, and its prognostic value for disease-free and overall survival. A pooled gene expression analysis of 39 publicly available transcriptomic datasets was also performed. RESULTS: Of the initial 4,184 entries, 38 retrospective studies fulfilled the predefined inclusion criteria. The overall pooled PD-L1 protein positivity rate was 24% (95% CI, 15%-33%) in tumor cells and 33% (95% CI, 14%- 56%) in immune cells. PD-L1 protein expression in tumor cells was prognostic for shorter overall survival (HR, 1.63; 95% CI, 1.07-2.46; P = 0.02); there was significant heterogeneity (I2 = 80%, P heterogeneity < 0.001). In addition, higher PD-L1 gene expression predicted better survival in multivariate analysis in the entire population (HR, 0.82; 95% CI, 0.74-0.90; P < 0.001 for OS) and in basal-like tumors (HR, 0.64; 95% CI, 0.52-0.80; P < 0.001 for OS; P interaction 0.005). CONCLUSIONS: The largest to our knowledge meta-analysis on the subject informs on PD-L1 protein positivity rates and its prognostic value in breast cancer. Standardization is needed prior to routine implementation. PD-L1 gene expression is a promising prognostic factor, especially in basal-like breast cancer. Discrepant prognostic information might be related to PD-L1 gene expression in the stroma.

Beyond PD-1/PD-L1 Inhibition: What the Future Holds for Breast Cancer Immunotherapy.

Cancer immunotherapy has altered the management of human malignancies, improving outcomes in an expanding list of diseases. Breast cancer - presumably due to its perceived low immunogenicity - is a late addition to this list. Furthermore, most of the focus has been on the triple negative subtype because of its higher tumor mutational load and lymphocyte-enriched stroma, although emerging data show promise on the other breast cancer subtypes as well. To this point the clinical use of immunotherapy is limited to the inhibition of two immune checkpoints, Programmed Cell Death Protein 1 (PD-1) and Cytotoxic T-lymphocyte-associated Protein 4 (CTLA-4). Consistent with the complexity of the regulation of the tumor - host interactions and their lack of reliance on a single regulatory pathway, combinatory approaches have shown improved efficacy albeit at the cost of increased toxicity. Beyond those two checkpoints though, a large number of co-stimulatory or co-inhibitory molecules play major roles on tumor evasion from immunosurveillance. These molecules likely represent future targets of immunotherapy provided that the promise shown in early data is translated into improved patient survival in randomized trials. The biological role, prognostic and predictive implications regarding breast cancer and early clinical efforts on exploiting these immune-related therapeutic targets are herein reviewed.