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Invasive lobular carcinoma (ILC) is the most common of the breast cancer special types, accounting for up to 15% of all breast cancer cases. ILCs are noted for their lack of E-cadherin function, which underpins their characteristic discohesive growth pattern, with cells arranged in single file and dispersed throughout the stroma. Typically, tumours are luminal in molecular subtype, being oestrogen and progesterone receptor positive, and HER2 negative. Since last reviewing the lobular literature (McCart Reed et al., Breast Cancer Res 17:12, 2015), there has been a considerable increase in research output focused on this tumour type, including studies into the pathology and management of disease, a high-resolution definition of the genomic landscape of tumours as well as the evolution of several potential therapeutic avenues. There abounds a huge amount of new data, which we will review herein.

Lobular neoplasia (LN) is an atypical proliferation of small, dyscohesive epithelial cells within the terminal duct lobular unit (TDLU), with or without pagetoid extension and encompasses both lobular carcinoma in situ (LCIS) and atypical lobular hyperplasia (ALH). LN is a non-obligate precursor of invasive breast carcinoma and the diagnosis of LN confers an increased risk of invasive carcinoma development, compared to the general population. Diagnostic challenges arise in the accurate classification of LCIS into classic, pleomorphic and florid subtypes, in distinguishing between LCIS and ductal carcinoma in situ (DCIS) and in the appropriate use and interpretation of E-cadherin immunohistochemistry. Due to the paucity of robust data on the natural history of LCIS, and hence its clinical significance, the management is often pragmatic rather than entirely evidence-based and requires a multidisciplinary approach. In this review, we discuss the clinicopathologic and molecular features of LCIS and address the key challenges that arise in the diagnosis and management of LCIS.

To update key recommendations of the American Society of Clinical Oncology/College of American Pathologists estrogen receptor (ER) and progesterone receptor (PgR) testing in breast cancer guideline. A multidisciplinary international Expert Panel was convened to update the clinical practice guideline recommendations informed by a systematic review of the medical literature. The Expert Panel continues to recommend ER testing of invasive breast cancers by validated immunohistochemistry as the standard for predicting which patients may benefit from endocrine therapy, and no other assays are recommended for this purpose. Breast cancer samples with 1% to 100% of tumor nuclei positive should be interpreted as ER positive. However, the Expert Panel acknowledges that there are limited data on endocrine therapy benefit for cancers with 1% to 10% of cells staining ER positive. Samples with these results should be reported using a new reporting category, ER Low Positive, with a recommended comment. A sample is considered ER negative if < 1% or 0% of tumor cell nuclei are immunoreactive. Additional strategies recommended to promote optimal performance, interpretation, and reporting of cases with an initial low to no ER staining result include establishing a laboratory-specific standard operating procedure describing additional steps used by the laboratory to confirm/adjudicate results. The status of controls should be reported for cases with 0% to 10% staining. Similar principles apply to PgR testing, which is used primarily for prognostic purposes in the setting of an ER-positive cancer. Testing of ductal carcinoma in situ (DCIS) for ER is recommended to determine potential benefit of endocrine therapies to reduce risk of future breast cancer, while testing DCIS for PgR is considered optional. Additional information can be found at www.asco.org/breast-cancer-guidelines .

HRDetect represents a model integrating whole-genome sequencing mutation signatures associated with BRCA1 and BRCA2 deficiency. The implementation of this predictor across different tumor types identifies a larger proportion of patients displaying ‘BRCAness’ than previously recognized; they might derive benefit from platinum and PARP-inhibitor therapies. Approximately 1–5% of breast cancers are attributed to inherited mutations in BRCA1 or BRCA2 and are selectively sensitive to poly(ADP-ribose) polymerase (PARP) inhibitors. In other cancer types, germline and/or somatic mutations in BRCA1 and/or BRCA2 ( BRCA1 / BRCA2 ) also confer selective sensitivity to PARP inhibitors. Thus, assays to detect BRCA1 / BRCA2 -deficient tumors have been sought. Recently, somatic substitution, insertion/deletion and rearrangement patterns, or 'mutational signatures', were associated with BRCA1 / BRCA2 dysfunction. Herein we used a lasso logistic regression model to identify six distinguishing mutational signatures predictive of BRCA1 / BRCA2 deficiency. A weighted model called HRDetect was developed to accurately detect BRCA1 / BRCA2 -deficient samples. HRDetect identifies BRCA1 / BRCA2 -deficient tumors with 98.7% sensitivity (area under the curve (AUC) = 0.98). Application of this model in a cohort of 560 individuals with breast cancer, of whom 22 were known to carry a germline BRCA1 or BRCA2 mutation, allowed us to identify an additional 22 tumors with somatic loss of BRCA1 or BRCA2 and 47 tumors with functional BRCA1 / BRCA2 deficiency where no mutation was detected. We validated HRDetect on independent cohorts of breast, ovarian and pancreatic cancers and demonstrated its efficacy in alternative sequencing strategies. Integrating all of the classes of mutational signatures thus reveals a larger proportion of individuals with breast cancer harboring BRCA1 / BRCA2 deficiency (up to 22%) than hitherto appreciated (∼1–5%) who could have selective therapeutic sensitivity to PARP inhibition.

Invasive lobular carcinoma of the breast is the most common ‘special’ morphological subtype of breast cancer, comprising up to 15% of all cases. Tumours are generally of a good prognostic phenotype, being low histological grade and low mitotic index, hormone receptor positive and HER2, p53 and basal marker negative, and with a generally good response to endocrine therapy. Despite this, clinicians face countless challenges in the diagnosis and long-term management of patients, as they encounter a tumour that can be difficult to detect through screening, elicits a very invasive nature, a propensity for widespread metastatic colonisation and, consequently, in some studies a worse long-term poor outcome compared with invasive carcinoma of no special type. Here we review the morphological and molecular features that underpin the disparate biological and clinical characteristics of this fascinating tumour type.

Spatial transcriptomics (ST) technologies generate multiple data types from biological samples, namely gene expression, physical distance between data points, and/or tissue morphology. Here we developed three computational-statistical algorithms that integrate all three data types to advance understanding of cellular processes. First, we present a spatial graph-based method, pseudo-time-space (PSTS), to model and uncover relationships between transcriptional states of cells across tissues undergoing dynamic change (e.g. neurodevelopment, brain injury and/or microglia activation, and cancer progression). We further developed a spatially-constrained two-level permutation (SCTP) test to study cell-cell interaction, finding highly interactive tissue regions across thousands of ligand-receptor pairs with markedly reduced false discovery rates. Finally, we present a spatial graph-based imputation method with neural network (stSME), to correct for technical noise/dropout and increase ST data coverage. Together, the algorithms that we developed, implemented in the comprehensive and fast stLearn software, allow for robust interrogation of biological processes within healthy and diseased tissues. The integration of spatial, imaging, and sequencing information enables the mapping of cellular dynamics within a tissue. Here, authors show three algorithms in stLearn software to accurately reveal spatial trajectory, detect cell-cell interactions, and impute missing data.

N-myc downregulated gene-1 (NDRG1) has been variably reported as a metastasis suppressor, a biomarker of poor outcome, and a facilitator of disease progression in a range of different cancers. NDRG1 is poorly understood in cancer due to its context-dependent and pleiotropic functions. Within breast cancer, NDRG1 is reported to be either a facilitator of, or an inhibitor of tumour progression and metastasis. The wide array of roles played by NDRG1 are dependent on post-translational modifications and subcellular localization, as well as the cellular context, for example, cancer type. We present an update on NDRG1, and its association with hallmarks of cancer such as hypoxia, its interaction with oncogenic proteins such as p53 as well its role in oncogenic and metastasis pathways in breast and other cancers. We further comment on its functional implications as a metastasis suppressor and promoter, its clinical relevance, and discuss its therapeutic targetability in different cancers.

Somatic mutations in human cancers show unevenness in genomic distribution that correlate with aspects of genome structure and function. These mutations are, however, generated by multiple mutational processes operating through the cellular lineage between the fertilized egg and the cancer cell, each composed of specific DNA damage and repair components and leaving its own characteristic mutational signature on the genome. Using somatic mutation catalogues from 560 breast cancer whole-genome sequences, here we show that each of 12 base substitution, 2 insertion/deletion (indel) and 6 rearrangement mutational signatures present in breast tissue, exhibit distinct relationships with genomic features relating to transcription, DNA replication and chromatin organization. This signature-based approach permits visualization of the genomic distribution of mutational processes associated with APOBEC enzymes, mismatch repair deficiency and homologous recombinational repair deficiency, as well as mutational processes of unknown aetiology. Furthermore, it highlights mechanistic insights including a putative replication-dependent mechanism of APOBEC-related mutagenesis. Mutational signatures provide evidence of the mechanism of action of a given mutagen and are found in cancer cells. Here, using 560 breast cancer genomes, the authors demonstrate that mutational signatures are frequently associated with genomic architecture including nucleosome positioning and replication timing.

Retrospective studies provide conflicting interpretations of the effect of inherited genetic factors on the prognosis of patients with breast cancer. The primary aim of this study was to determine the effect of a germline BRCA1 or BRCA2 mutation on breast cancer outcomes in patients with young-onset breast cancer. We did a prospective cohort study of female patients recruited from 127 hospitals in the UK aged 40 years or younger at first diagnosis (by histological confirmation) of invasive breast cancer. Patients with a previous invasive malignancy (except non-melanomatous skin cancer) were excluded. Patients were identified within 12 months of initial diagnosis. BRCA1 and BRCA2 mutations were identified using blood DNA collected at recruitment. Clinicopathological data, and data regarding treatment and long-term outcomes, including date and site of disease recurrence, were collected from routine medical records at 6 months, 12 months, and then annually until death or loss to follow-up. The primary outcome was overall survival for all BRCA1 or BRCA2 mutation carriers (BRCA-positive) versus all non-carriers (BRCA-negative) at 2 years, 5 years, and 10 years after diagnosis. A prespecified subgroup analysis of overall survival was done in patients with triple-negative breast cancer. Recruitment was completed in 2008, and long-term follow-up is continuing. Between Jan 24, 2000, and Jan 24, 2008, we recruited 2733 women. Genotyping detected a pathogenic BRCA mutation in 338 (12%) patients (201 with BRCA1, 137 with BRCA2). After a median follow-up of 8·2 years (IQR 6·0–9·9), 651 (96%) of 678 deaths were due to breast cancer. There was no significant difference in overall survival between BRCA-positive and BRCA-negative patients in multivariable analyses at any timepoint (at 2 years: 97·0% [95% CI 94·5–98·4] vs 96·6% [95·8–97·3]; at 5 years: 83·8% [79·3–87·5] vs 85·0% [83·5–86·4]; at 10 years: 73·4% [67·4–78·5] vs 70·1% [67·7–72·3]; hazard ratio [HR] 0·96 [95% CI 0·76–1·22]; p=0·76). Of 558 patients with triple-negative breast cancer, BRCA mutation carriers had better overall survival than non-carriers at 2 years (95% [95% CI 89–97] vs 91% [88–94]; HR 0·59 [95% CI 0·35–0·99]; p=0·047) but not 5 years (81% [73–87] vs 74% [70–78]; HR 1·13 [0·70–1·84]; p=0·62) or 10 years (72% [62–80] vs 69% [63–74]; HR 2·12 [0·82–5·49]; p= 0·12). Patients with young-onset breast cancer who carry a BRCA mutation have similar survival as non-carriers. However, BRCA mutation carriers with triple-negative breast cancer might have a survival advantage during the first few years after diagnosis compared with non-carriers. Decisions about timing of additional surgery aimed at reducing future second primary-cancer risks should take into account patient prognosis associated with the first malignancy and patient preferences. Cancer Research UK, the UK National Cancer Research Network, the Wessex Cancer Trust, Breast Cancer Now, and the PPP Healthcare Medical Trust Grant.

Breast cancer is a complex, multifaceted disease encompassing a great variety of entities that show considerable variation in clinical, morphological and molecular attributes. Traditional classifications including histological assessment and clinical staging are used to guide patient management. In recent years, there has been exponential progress in molecular analysis with profound implications for our understanding of breast cancer biology and, hence, classification. There are now genome-based frameworks for the molecular categorisation of breast cancer including the development of prognostic and predictive signatures that potentially allow individualisation of treatment. Here we review the current state of the molecular classifications of in situ and invasive breast cancer including special subtypes. Future perspectives are also provided.