Natural History of Germline BRCA1 Mutated and BRCA Wild-type Triple-negative Breast Cancer.

We report a deep next-generation sequencing analysis of 13 sequentially obtained tumor samples, eight sequentially obtained circulating tumor DNA (ctDNA) samples and three germline DNA samples over the life history of 3 patients with triple-negative breast cancer (TNBC), 2 of whom had germline pathogenic BRCA1 mutation, to unravel tumor evolution. Tumor tissue from all timepoints and germline DNA was subjected to whole-exome sequencing (WES), custom amplicon deep sequencing (30,000X) of a WES-derived somatic mutation panel, and SNP arrays for copy-number variation (CNV), while whole transcriptome sequencing (RNA-seq) was performed only on somatic tumor.There was enrichment of homologous recombination deficiency signature in all tumors and widespread CNV, which remained largely stable over time. Somatic tumor mutation numbers varied between patients and within each patient (range: 70-216, one outlier). There was minimal mutational overlap between patients with TP53 being the sole commonly mutated gene, but there was substantial overlap in sequential samples in each patient. Each patient's tumor contained a founding ("stem") clone at diagnosis, which persisted over time, from which all other clones ("subclone") were derived ("branching evolution"), which contained mutations in well-characterized cancer-related genes like PDGFRB, ARID2, TP53 (Patient_02), TP53, BRAF, BRIP1, CSF3R (Patient_04), and TP53, APC, EZH2 (Patient_07). Including stem and subclones, tumors from all patients were polyclonal at diagnosis and during disease progression. ctDNA recapitulated most tissue-derived stem clonal and subclonal mutations while detecting some additional subclonal mutations. RNA-seq revealed a stable basal-like pattern, with most highly expressed variants belonging to stem clone. SIGNIFICANCE: In germline BRCA1 mutated and BRCA wild-type patients, TNBC shows a branching evolutionary pattern of mutations with a single founding clone, are polyclonal throughout their disease course, and have widespread copy-number aberrations. This evolutionary pattern may be associated with treatment resistance or sensitivity and could be therapeutically exploited.

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.

Adipose-enriched peri-tumoral stroma, in contrast to myofibroblast-enriched stroma, prognosticates poorer survival in breast cancers.

Despite our understanding of the genetic basis of intra-tumoral heterogeneity, the role of stromal heterogeneity arising from an altered tumor microenvironment in affecting tumorigenesis is poorly understood. In particular, extensive study on the peri-tumoral stroma in the morphologically normal tissues surrounding the tumor is lacking. Here, we examine the heterogeneity in tumors and peri-tumoral stroma from 8 ER+/PR+/HER2- invasive breast carcinomas, through multi-region transcriptomic profiling by microarray. We describe the regional heterogeneity observed at the intrinsic molecular subtype, pathway enrichment, and cell type composition levels within each tumor and its peri-tumoral region, up to 7 cm from the tumor margins. Moreover, we identify a pro-inflammatory adipose-enriched peri-tumoral subtype which was significantly associated with poorer overall survival in breast cancer patients, in contrast to an adaptive immune cell- and myofibroblast-enriched subtype. These data together suggest that peri-tumoral heterogeneity may be an important determinant of the evolution and treatment of breast cancers.

Leveraging circulating microbial DNA for early cancer detection.

Microbial cell-free DNA (microbial cfDNA) offers a minimally invasive approach for profiling the microbiome. Despite technical and biological challenges, the potential of microbial cfDNA for cancer detection is increasingly being evaluated using deep sequencing, novel laboratory approaches, and computational methods. Targeting the microbiome using liquid biopsies could improve early cancer detection.

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.

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.

Integrated Multimodal Analyses of DNA Damage Response and Immune Markers as Predictors of Response in Metastatic Triple-Negative Breast Cancer in the TNT Trial (NCT00532727).

PURPOSE: The TNT trial (NCT00532727) showed no evidence of carboplatin superiority over docetaxel in metastatic triple-negative breast cancer (mTNBC), but carboplatin benefit was observed in the germline BRCA1/2 mutation subgroup. Broader response-predictive biomarkers are needed. We explored the predictive ability of DNA damage response (DDR) and immune markers. EXPERIMENTAL DESIGN: Tumor-infiltrating lymphocytes were evaluated for 222 of 376 patients. Primary tumors (PT) from 186 TNT participants (13 matched recurrences) were profiled using total RNA sequencing. Four transcriptional DDR-related and 25 immune-related signatures were evaluated. We assessed their association with objective response rate (ORR) and progression-free survival (PFS). Conditional inference forest clustering was applied to integrate multimodal data. The biology of subgroups was characterized by 693 gene expression modules and other markers. RESULTS: Transcriptional DDR-related biomarkers were not predictive of ORR to either treatment overall. Changes from PT to recurrence were demonstrated; in chemotherapy-naïve patients, transcriptional DDR markers separated carboplatin responders from nonresponders (P values = 0.017; 0.046). High immune infiltration was associated with docetaxel ORR (interaction P values < 0.05). Six subgroups were identified; the immune-enriched cluster had preferential docetaxel response [62.5% (D) vs. 29.4% (C); P = 0.016]. The immune-depleted cluster had preferential carboplatin response [8.0% (D) vs. 40.0% (C); P = 0.011]. DDR-related subgroups were too small to assess ORR. CONCLUSIONS: High immune features predict docetaxel response, and high DDR signature scores predict carboplatin response in treatment-naïve mTNBC. Integrating multimodal DDR and immune-related markers identifies subgroups with differential treatment sensitivity. Treatment options for patients with immune-low and DDR-proficient tumors remains an outstanding need. Caution is needed using PT-derived transcriptional signatures to direct treatment in mTNBC, particularly DDR-related markers following prior chemotherapy.

Computational pathology in cancer diagnosis, prognosis, and prediction - present day and prospects.

Computational pathology refers to applying deep learning techniques and algorithms to analyse and interpret histopathology images. Advances in artificial intelligence (AI) have led to an explosion in innovation in computational pathology, ranging from the prospect of automation of routine diagnostic tasks to the discovery of new prognostic and predictive biomarkers from tissue morphology. Despite the promising potential of computational pathology, its integration in clinical settings has been limited by a range of obstacles including operational, technical, regulatory, ethical, financial, and cultural challenges. Here, we focus on the pathologists' perspective of computational pathology: we map its current translational research landscape, evaluate its clinical utility, and address the more common challenges slowing clinical adoption and implementation. We conclude by describing contemporary approaches to drive forward these techniques. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

B cell profiles, antibody repertoire and reactivity reveal dysregulated responses with autoimmune features in melanoma.

B cells are known to contribute to the anti-tumor immune response, especially in immunogenic tumors such as melanoma, yet humoral immunity has not been characterized in these cancers to detail. Here we show comprehensive phenotyping in samples of circulating and tumor-resident B cells as well as serum antibodies in melanoma patients. Memory B cells are enriched in tumors compared to blood in paired samples and feature distinct antibody repertoires, linked to specific isotypes. Tumor-associated B cells undergo clonal expansion, class switch recombination, somatic hypermutation and receptor revision. Compared with blood, tumor-associated B cells produce antibodies with proportionally higher levels of unproductive sequences and distinct complementarity determining region 3 properties. The observed features are signs of affinity maturation and polyreactivity and suggest an active and aberrant autoimmune-like reaction in the tumor microenvironment. Consistent with this, tumor-derived antibodies are polyreactive and characterized by autoantigen recognition. Serum antibodies show reactivity to antigens attributed to autoimmune diseases and cancer, and their levels are higher in patients with active disease compared to post-resection state. Our findings thus reveal B cell lineage dysregulation with distinct antibody repertoire and specificity, alongside clonally-expanded tumor-infiltrating B cells with autoimmune-like features, shaping the humoral immune response in melanoma.

Multiscale deep learning framework captures systemic immune features in lymph nodes predictive of triple negative breast cancer outcome in large-scale studies.

The suggestion that the systemic immune response in lymph nodes (LNs) conveys prognostic value for triple-negative breast cancer (TNBC) patients has not previously been investigated in large cohorts. We used a deep learning (DL) framework to quantify morphological features in haematoxylin and eosin-stained LNs on digitised whole slide images. From 345 breast cancer patients, 5,228 axillary LNs, cancer-free and involved, were assessed. Generalisable multiscale DL frameworks were developed to capture and quantify germinal centres (GCs) and sinuses. Cox regression proportional hazard models tested the association between smuLymphNet-captured GC and sinus quantifications and distant metastasis-free survival (DMFS). smuLymphNet achieved a Dice coefficient of 0.86 and 0.74 for capturing GCs and sinuses, respectively, and was comparable to an interpathologist Dice coefficient of 0.66 (GC) and 0.60 (sinus). smuLymphNet-captured sinuses were increased in LNs harbouring GCs (p < 0.001). smuLymphNet-captured GCs retained clinical relevance in LN-positive TNBC patients whose cancer-free LNs had on average ≥2 GCs, had longer DMFS (hazard ratio [HR] = 0.28, p = 0.02) and extended GCs' prognostic value to LN-negative TNBC patients (HR = 0.14, p = 0.002). Enlarged smuLymphNet-captured sinuses in involved LNs were associated with superior DMFS in LN-positive TNBC patients in a cohort from Guy's Hospital (multivariate HR = 0.39, p = 0.039) and with distant recurrence-free survival in 95 LN-positive TNBC patients of the Dutch-N4plus trial (HR = 0.44, p = 0.024). Heuristic scoring of subcapsular sinuses in LNs of LN-positive Tianjin TNBC patients (n = 85) cross-validated the association of enlarged sinuses with shorter DMFS (involved LNs: HR = 0.33, p = 0.029 and cancer-free LNs: HR = 0.21 p = 0.01). Morphological LN features reflective of cancer-associated responses are robustly quantifiable by smuLymphNet. Our findings further strengthen the value of assessment of LN properties beyond the detection of metastatic deposits for prognostication of TNBC patients. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

B Cells in Breast Cancer Pathology.

B cells have recently become a focus in breast cancer pathology due to their influence on tumour regression, prognosis, and response to treatment, besides their contribution to antigen presentation, immunoglobulin production, and regulation of adaptive responses. As our understanding of diverse B cell subsets in eliciting both pro- and anti-inflammatory responses in breast cancer patients increases, it has become pertinent to address the molecular and clinical relevance of these immune cell populations within the tumour microenvironment (TME). At the primary tumour site, B cells are either found spatially dispersed or aggregated in so-called tertiary lymphoid structures (TLS). In axillary lymph nodes (LNs), B cell populations, amongst a plethora of activities, undergo germinal centre reactions to ensure humoral immunity. With the recent approval for the addition of immunotherapeutic drugs as a treatment option in the early and metastatic settings for triple-negative breast cancer (TNBC) patients, B cell populations or TLS may resemble valuable biomarkers for immunotherapy responses in certain breast cancer subgroups. New technologies such as spatially defined sequencing techniques, multiplex imaging, and digital technologies have further deciphered the diversity of B cells and the morphological structures in which they appear in the tumour and LNs. Thus, in this review, we comprehensively summarise the current knowledge of B cells in breast cancer. In addition, we provide a user-friendly single-cell RNA-sequencing platform, called "B singLe cEll rna-Seq browSer" (BLESS) platform, with a focus on the B cells in breast cancer patients to interrogate the latest publicly available single-cell RNA-sequencing data collected from diverse breast cancer studies. Finally, we explore their clinical relevance as biomarkers or molecular targets for future interventions.

Leveraging the Dynamic Immune Environment Triad in Patients with Breast Cancer: Tumour, Lymph Node, and Peripheral Blood.

During the anti-tumour response to breast cancer, the primary tumour, the peripheral blood, and the lymph nodes each play unique roles. Immunological features at each site reveal evidence of continuous immune cross-talk between them before, during and after treatment. As such, immune responses to breast cancer are found to be highly dynamic and truly systemic, integrating three distinct immune sites, complex cell-migration highways, as well as the temporal dimension of disease progression and treatment. In this review, we provide a connective summary of the dynamic immune environment triad of breast cancer. It is critical that future studies seek to establish dynamic immune profiles, constituting multiple sites, that capture the systemic immune response to breast cancer and define patient-selection parameters resulting in more significant overall responses and survival rates for breast cancer patients.

CDK Inhibition Primes for Anti-PD-L1 Treatment in Triple-Negative Breast Cancer Models.

Triple-negative breast cancers (TNBC) expressing PD-L1 qualify for checkpoint inhibitor immunotherapy. Cyclin E/CDK2 is a potential target axis in TNBC; however, small-molecule drugs at efficacious doses may be associated with toxicity, and treatment alongside immunotherapy requires investigation. We evaluated CDK inhibition at suboptimal levels and its anti-tumor and immunomodulatory effects. Transcriptomic analyses of primary breast cancers confirmed higher cyclin E/CDK2 expression in TNBC compared with non-TNBC. Out of the three CDK2-targeting inhibitors tested, the CDK 2, 7 and 9 inhibitor SNS-032 was the most potent in reducing TNBC cell viability and exerted cytotoxicity against all eight TNBC cell lines evaluated in vitro. Suboptimal SNS-032 dosing elevated cell surface PD-L1 expression in surviving TNBC cells. In mice engrafted with human immune cells and challenged with human MDA-MB-231 TNBC xenografts in mammary fat pads, suboptimal SNS-032 dosing partially restricted tumor growth, enhanced the tumor infiltration of human CD45+ immune cells and elevated cell surface PD-L1 expression in surviving cancer cells. In tumor-bearing mice engrafted with human immune cells, the anti-PD-L1 antibody avelumab, given sequentially following suboptimal SNS-032 dosing, reduced tumor growth compared with SNS-032 alone or with avelumab without prior SNS-032 priming. CDK inhibition at suboptimal doses promotes immune cell recruitment to tumors, PD-L1 expression by surviving TNBC cells and may complement immunotherapy.

Functional screening reveals HORMAD1-driven gene dependencies associated with translesion synthesis and replication stress tolerance.

HORMAD1 expression is usually restricted to germline cells, but it becomes mis-expressed in epithelial cells in ~60% of triple-negative breast cancers (TNBCs), where it is associated with elevated genomic instability (1). HORMAD1 expression in TNBC is bimodal with HORMAD1-positive TNBC representing a biologically distinct disease group. Identification of HORMAD1-driven genetic dependencies may uncover novel therapies for this disease group. To study HORMAD1-driven genetic dependencies, we generated a SUM159 cell line model with doxycycline-inducible HORMAD1 that replicated genomic instability phenotypes seen in HORMAD1-positive TNBC (1). Using small interfering RNA screens, we identified candidate genes whose depletion selectively inhibited the cellular growth of HORMAD1-expressing cells. We validated five genes (ATR, BRIP1, POLH, TDP1 and XRCC1), depletion of which led to reduced cellular growth or clonogenic survival in cells expressing HORMAD1. In addition to the translesion synthesis (TLS) polymerase POLH, we identified a HORMAD1-driven dependency upon additional TLS polymerases, namely POLK, REV1, REV3L and REV7. Our data confirms that out-of-context somatic expression of HORMAD1 can lead to genomic instability and reveals that HORMAD1 expression induces dependencies upon replication stress tolerance pathways, such as translesion synthesis. Our data also suggest that HORMAD1 expression could be a patient selection biomarker for agents targeting replication stress.

Inducible localized delivery of an anti-PD-1 scFv enhances anti-tumor activity of ROR1 CAR-T cells in TNBC.

BACKGROUND: Chimeric antigen receptor (CAR)-T cells can induce powerful immune responses in patients with hematological malignancies but have had limited success against solid tumors. This is in part due to the immunosuppressive tumor microenvironment (TME) which limits the activity of tumor-infiltrating lymphocytes (TILs) including CAR-T cells. We have developed a next-generation armored CAR (F i-CAR) targeting receptor tyrosine kinase-like orphan receptor 1 (ROR1), which is expressed at high levels in a range of aggressive tumors including poorly prognostic triple-negative breast cancer (TNBC). The F i-CAR-T is designed to release an anti-PD-1 checkpoint inhibitor upon CAR-T cell activation within the TME, facilitating activation of CAR-T cells and TILs while limiting toxicity. METHODS: To bolster potency, we developed a F i-CAR construct capable of IL-2-mediated, NFAT-induced secretion of anti-PD-1 single-chain variable fragments (scFv) within the tumor microenvironment, following ROR1-mediated activation. Cytotoxic responses against TNBC cell lines as well as levels and binding functionality of released payload were analyzed in vitro by ELISA and flow cytometry. In vivo assessment of potency of F i-CAR-T cells was performed in a TNBC NSG mouse model. RESULTS: F i-CAR-T cells released measurable levels of anti-PD-1 payload with 5 h of binding to ROR1 on tumor and enhanced the cytotoxic effects at challenging 1:10 E:T ratios. Treatment of established PDL1 + TNBC xenograft model with F i-CAR-T cells resulted in significant abrogation in tumor growth and improved survival of mice (71 days), compared to non-armored CAR cells targeting ROR1 (F CAR-T) alone (49 days) or in combination with systemically administered anti-PD-1 antibody (57 days). Crucially, a threefold increase in tumor-infiltrating T cells was observed with F i-CAR-T cells and was associated with increased expression of genes related to cytotoxicity, migration and proliferation. CONCLUSIONS: Our next-generation of ROR1-targeting inducible armored CAR platform enables the release of an immune stimulating payload only in the presence of target tumor cells, enhancing the therapeutic activity of the CAR-T cells. This technology provided a significant survival advantage in TNBC xenograft models. This coupled with its potential safety attributes merits further clinical evaluation of this approach in TNBC patients.

Genomic analysis defines clonal relationships of ductal carcinoma in situ and recurrent invasive breast cancer.

Ductal carcinoma in situ (DCIS) is the most common form of preinvasive breast cancer and, despite treatment, a small fraction (5-10%) of DCIS patients develop subsequent invasive disease. A fundamental biologic question is whether the invasive disease arises from tumor cells in the initial DCIS or represents new unrelated disease. To address this question, we performed genomic analyses on the initial DCIS lesion and paired invasive recurrent tumors in 95 patients together with single-cell DNA sequencing in a subset of cases. Our data show that in 75% of cases the invasive recurrence was clonally related to the initial DCIS, suggesting that tumor cells were not eliminated during the initial treatment. Surprisingly, however, 18% were clonally unrelated to the DCIS, representing new independent lineages and 7% of cases were ambiguous. This knowledge is essential for accurate risk evaluation of DCIS, treatment de-escalation strategies and the identification of predictive biomarkers.

Tumor-Infiltrating B Lymphocyte Profiling Identifies IgG-Biased, Clonally Expanded Prognostic Phenotypes in Triple-Negative Breast Cancer.

In breast cancer, humoral immune responses may contribute to clinical outcomes, especially in more immunogenic subtypes. Here, we investigated B lymphocyte subsets, immunoglobulin expression, and clonal features in breast tumors, focusing on aggressive triple-negative breast cancers (TNBC). In samples from patients with TNBC and healthy volunteers, circulating and tumor-infiltrating B lymphocytes (TIL-B) were evaluated. CD20+CD27+IgD- isotype-switched B lymphocytes were increased in tumors, compared with matched blood. TIL-B frequently formed stromal clusters with T lymphocytes and engaged in bidirectional functional cross-talk, consistent with gene signatures associated with lymphoid assembly, costimulation, cytokine-cytokine receptor interactions, cytotoxic T-cell activation, and T-cell-dependent B-cell activation. TIL-B-upregulated B-cell receptor (BCR) pathway molecules FOS and JUN, germinal center chemokine regulator RGS1, activation marker CD69, and TNFα signal transduction via NFκB, suggesting BCR-immune complex formation. Expression of genes associated with B lymphocyte recruitment and lymphoid assembly, including CXCL13, CXCR4, and DC-LAMP, was elevated in TNBC compared with other subtypes and normal breast. TIL-B-rich tumors showed expansion of IgG but not IgA isotypes, and IgG isotype switching positively associated with survival outcomes in TNBC. Clonal expansion was biased toward IgG, showing expansive clonal families with specific variable region gene combinations and narrow repertoires. Stronger positive selection pressure was present in the complementarity determining regions of IgG compared with their clonally related IgA in tumor samples. Overall, class-switched B lymphocyte lineage traits were conspicuous in TNBC, associated with improved clinical outcomes, and conferred IgG-biased, clonally expanded, and likely antigen-driven humoral responses. SIGNIFICANCE: Tumor-infiltrating B lymphocytes assemble in clusters, undergoing B-cell receptor-driven activation, proliferation, and isotype switching. Clonally expanded, IgG isotype-biased humoral immunity associates with favorable prognosis primarily in triple-negative breast cancers.

Systemic immune reaction in axillary lymph nodes adds to tumor-infiltrating lymphocytes in triple-negative breast cancer prognostication.

The level of stromal tumor-infiltrating lymphocytes (sTILs) in triple-negative (TNBC) and HER2-positive breast cancers convey prognostic information. The importance of systemic immunity to local immunity is unknown in breast cancer. We previously demonstrated that histological alterations in axillary lymph nodes (LNs) carry clinical relevance. Here, we capture local immune responses by scoring TILs at the primary tumor and systemic immune responses by recording the formation of secondary follicles, also known as germinal centers, in 2,857 cancer-free and involved axillary LNs on haematoxylin and eosin (H&E) stained sections from a retrospective cohort of 161 LN-positive triple-negative and HER2-positive breast cancer patients. Our data demonstrate that the number of germinal center formations across all cancer-free LNs, similar to high levels of TILs, is associated with a good prognosis in low TILs TNBC. This highlights the importance of assessing both primary and LN immune responses for prognostication and for future breast cancer research.

Immune Crosstalk Between Lymph Nodes and Breast Carcinomas, With a Focus on B Cells.

Lymph nodes (LNs) are highly organized secondary lymphoid organs, and reflective of immune responses to infection, injuries, or the presence of cancer. Extensive molecular and morphological analyses of immune and stromal features in tumors and LNs of breast cancer patients have revealed novel patterns indicative of disease progression. Within LNs, there are dynamic structures called germinal centers (GCs), that act as the immunological hubs for B cell development and generation of affinity matured memory B and antibody-producing plasma cells. Acting as a bridge between systemic and local immunity, associations are observed between the frequency of GCs within cancer-free LNs, the levels of stromal tumor infiltrating lymphocytes, and cancer progression. Scattered throughout the tumor microenvironment (TME) or aggregated in clusters forming tertiary lymphoid structures (TLS), the occurrence of tumor infiltrating B cells (TIL-Bs) has been linked mostly to superior disease trajectories in solid cancers. Recent TIL-Bs profiling studies have revealed a plethora of different TIL-B populations, their functional roles, and whether they are derived from GC reactions in the LN, and/or locally from GC-like structures within the TME remains to be investigated. However, parallels between the immunogenic nature of LNs as a pre-metastatic niche, TIL-B populations within the TME, and the presence of TLS will help to decipher local and widespread TIL-Bs responses and their influence on cancer progression to the lymphatics. Therapies that enhance TIL-Bs responses in the LN GC and/or in GC-like structures in the TME are thus emerging management strategies for breast and other cancer patients.

Random Forest Modelling of High-Dimensional Mixed-Type Data for Breast Cancer Classification.

Advances in high-throughput technologies encourage the generation of large amounts of multiomics data to investigate complex diseases, including breast cancer. Given that the aetiologies of such diseases extend beyond a single biological entity, and that essential biological information can be carried by all data regardless of data type, integrative analyses are needed to identify clinically relevant patterns. To facilitate such analyses, we present a permutation-based framework for random forest methods which simultaneously allows the unbiased integration of mixed-type data and assessment of relative feature importance. Through simulation studies and machine learning datasets, the performance of the approach was evaluated. The results showed minimal multicollinearity and limited overfitting. To further assess the performance, the permutation-based framework was applied to high-dimensional mixed-type data from two independent breast cancer cohorts. Reproducibility and robustness of our approach was demonstrated by the concordance in relative feature importance between the cohorts, along with consistencies in clustering profiles. One of the identified clusters was shown to be prognostic for clinical outcome after standard-of-care adjuvant chemotherapy and outperformed current intrinsic molecular breast cancer classifications.