Spatial molecular profiling of mixed invasive ductal and lobular breast cancers reveals heterogeneity in intrinsic molecular subtypes, oncogenic signatures, and mutations.

Mixed invasive ductal and lobular carcinoma (MDLC) is a rare histologic subtype of breast cancer displaying both E-cadherin positive ductal and E-cadherin negative lobular morphologies within the same tumor, posing challenges with regard to anticipated clinical management. It remains unclear whether these distinct morphologies also have distinct biology and risk of recurrence. Our spatially resolved transcriptomic, genomic, and single-cell profiling revealed clinically significant differences between ductal and lobular tumor regions including distinct intrinsic subtype heterogeneity - e.g., MDLC with triple-negative breast cancer (TNBC) or basal ductal and estrogen receptor positive (ER+) luminal lobular regions, distinct enrichment of cell cycle arrest/senescence and oncogenic (ER and MYC) signatures, genetic and epigenetic CDH1 inactivation in lobular but not ductal regions, and single-cell ductal and lobular subpopulations with unique oncogenic signatures further highlighting intraregional heterogeneity. Altogether, we demonstrated that the intratumoral morphological/histological heterogeneity within MDLC is underpinned by intrinsic subtype and oncogenic heterogeneity which may result in prognostic uncertainty and therapeutic dilemma.

Systems approach for congruence and selection of cancer models towards precision medicine.

Cancer models are instrumental as a substitute for human studies and to expedite basic, translational, and clinical cancer research. For a given cancer type, a wide selection of models, such as cell lines, patient-derived xenografts, organoids and genetically modified murine models, are often available to researchers. However, how to quantify their congruence to human tumors and to select the most appropriate cancer model is a largely unsolved issue. Here, we present Congruence Analysis and Selection of CAncer Models (CASCAM), a statistical and machine learning framework for authenticating and selecting the most representative cancer models in a pathway-specific manner using transcriptomic data. CASCAM provides harmonization between human tumor and cancer model omics data, systematic congruence quantification, and pathway-based topological visualization to determine the most appropriate cancer model selection. The systems approach is presented using invasive lobular breast carcinoma (ILC) subtype and suggesting CAMA1 followed by UACC3133 as the most representative cell lines for ILC research. Two additional case studies for triple negative breast cancer (TNBC) and patient-derived xenograft/organoid (PDX/PDO) are further investigated. CASCAM is generalizable to any cancer subtype and will authenticate cancer models for faithful non-human preclinical research towards precision medicine.

Research autopsy programmes in oncology: shared experience from 14 centres across the world.

While there is a great clinical need to understand the biology of metastatic cancer in order to treat it more effectively, research is hampered by limited sample availability. Research autopsy programmes can crucially advance the field through synchronous, extensive, and high-volume sample collection. However, it remains an underused strategy in translational research. Via an extensive questionnaire, we collected information on the study design, enrolment strategy, study conduct, sample and data management, and challenges and opportunities of research autopsy programmes in oncology worldwide. Fourteen programmes participated in this study. Eight programmes operated 24 h/7 days, resulting in a lower median postmortem interval (time between death and start of the autopsy, 4 h) compared with those operating during working hours (9 h). Most programmes (n = 10) succeeded in collecting all samples within a median of 12 h after death. A large number of tumour sites were sampled during each autopsy (median 15.5 per patient). The median number of samples collected per patient was 58, including different processing methods for tumour samples but also non-tumour tissues and liquid biopsies. Unique biological insights derived from these samples included metastatic progression, treatment resistance, disease heterogeneity, tumour dormancy, interactions with the tumour micro-environment, and tumour representation in liquid biopsies. Tumour patient-derived xenograft (PDX) or organoid (PDO) models were additionally established, allowing for drug discovery and treatment sensitivity assays. Apart from the opportunities and achievements, we also present the challenges related with postmortem sample collections and strategies to overcome them, based on the shared experience of these 14 programmes. Through this work, we hope to increase the transparency of postmortem tissue donation, to encourage and aid the creation of new programmes, and to foster collaborations on these unique sample collections. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

iGenSig-Rx: an integral genomic signature based white-box tool for modeling cancer therapeutic responses using multi-omics data.

Multi-omics sequencing is poised to revolutionize clinical care in the coming decade. However, there is a lack of effective and interpretable genome-wide modeling methods for the rational selection of patients for personalized interventions. To address this, we present iGenSig-Rx, an integral genomic signature-based approach, as a transparent tool for modeling therapeutic response using clinical trial datasets. This method adeptly addresses challenges related to cross-dataset modeling by capitalizing on high-dimensional redundant genomic features, analogous to reinforcing building pillars with redundant steel rods. Moreover, it integrates adaptive penalization of feature redundancy on a per-sample basis to prevent score flattening and mitigate overfitting. We then developed a purpose-built R package to implement this method for modeling clinical trial datasets. When applied to genomic datasets for HER2 targeted therapies, iGenSig-Rx model demonstrates consistent and reliable predictive power across four independent clinical trials. More importantly, the iGenSig-Rx model offers the level of transparency much needed for clinical application, allowing for clear explanations as to how the predictions are produced, how the features contribute to the prediction, and what are the key underlying pathways. We anticipate that iGenSig-Rx, as an interpretable class of multi-omics modeling methods, will find broad applications in big-data based precision oncology. The R package is available: https://github.com/wangxlab/iGenSig-Rx .

Highlight of 2023: CAR T cells driving precision therapy for autoimmune disease.

CAR T cell therapy is showing remarkable results in autoimmune disease with treatment-refractory patients showing durable drug-free remission. Here, we highlight five key papers from 2023 that are driving the development of CAR T cells to improve precision, safety, efficacy and accessibility for the treatment of autoantibody-associated autoimmune diseases.

Liver tropism of ER mutant breast cancer is characterized by unique molecular changes and immune infiltration.

PURPOSE: Hotspot estrogen receptor alpha (ER/ESR1) mutations are recognized as the driver for both endocrine resistance and metastasis in advanced ER-positive (ER+) breast cancer, but their contributions to metastatic organ tropism remain insufficiently understood. In this study, we aim to comprehensively profile the organotropic metastatic pattern for ESR1 mutant breast cancer. METHODS: The organ-specific metastatic pattern of ESR1 mutant breast cancer was delineated using multi-omics data from multiple publicly available cohorts of ER+ metastatic breast cancer patients. Gene mutation/copy number variation (CNV) and differential gene expression analyses were performed to identify the genomic and transcriptomic alterations uniquely associated with ESR1 mutant liver metastasis. Upstream regulator, downstream pathway, and immune infiltration analysis were conducted for subsequent mechanistic investigations. RESULTS: ESR1 mutation-driven liver tropism was revealed by significant differences, encompassing a higher prevalence of liver metastasis in patients with ESR1 mutant breast cancer and an enrichment of mutations in liver metastatic samples. The significant enrichment of AGO2 copy number amplifications (CNAs) and multiple gene expression changes were revealed uniquely in ESR1 mutant liver metastasis. We also unveiled alterations in downstream signaling pathways and immune infiltration, particularly an enrichment of neutrophils, suggesting potential therapeutic vulnerabilities. CONCLUSION: Our data provide a comprehensive characterization of the behaviors and mechanisms of ESR1 mutant liver metastasis, paving the way for the development of personalized therapy to target liver metastasis for patients with ESR1 mutant breast cancer.

Children with developmental dyslexia have equivalent audiovisual speech perception performance but their perceptual weights differ.

As reading is inherently a multisensory, audiovisual (AV) process where visual symbols (i.e., letters) are connected to speech sounds, the question has been raised whether individuals with reading difficulties, like children with developmental dyslexia (DD), have broader impairments in multisensory processing. This question has been posed before, yet it remains unanswered due to (a) the complexity and contentious etiology of DD along with (b) lack of consensus on developmentally appropriate AV processing tasks. We created an ecologically valid task for measuring multisensory AV processing by leveraging the natural phenomenon that speech perception improves when listeners are provided visual information from mouth movements (particularly when the auditory signal is degraded). We designed this AV processing task with low cognitive and linguistic demands such that children with and without DD would have equal unimodal (auditory and visual) performance. We then collected data in a group of 135 children (age 6.5-15) with an AV speech perception task to answer the following questions: (1) How do AV speech perception benefits manifest in children, with and without DD? (2) Do children all use the same perceptual weights to create AV speech perception benefits, and (3) what is the role of phonological processing in AV speech perception? We show that children with and without DD have equal AV speech perception benefits on this task, but that children with DD rely less on auditory processing in more difficult listening situations to create these benefits and weigh both incoming information streams differently. Lastly, any reported differences in speech perception in children with DD might be better explained by differences in phonological processing than differences in reading skills. RESEARCH HIGHLIGHTS: Children with versus without developmental dyslexia have equal audiovisual speech perception benefits, regardless of their phonological awareness or reading skills. Children with developmental dyslexia rely less on auditory performance to create audiovisual speech perception benefits. Individual differences in speech perception in children might be better explained by differences in phonological processing than differences in reading skills.

Anti-reflection coating with mullite and Duroid for large-diameter cryogenic sapphire and alumina optics.

We developed a broadband two-layer anti-reflection (AR) coating for use on a sapphire half-wave plate (HWP) and an alumina infrared (IR) filter for the cosmic microwave background (CMB) polarimetry. Measuring the faint CMB B-mode signals requires maximizing the number of photons reaching the detectors and minimizing spurious polarization due to reflection with an off-axis incident angle. Sapphire and alumina have high refractive indices of 3.1 and are highly reflective without an AR coating. This paper presents the design, fabrication, quality control, and measured performance of an AR coating using thermally sprayed mullite and Duroid 5880LZ. This technology enables large optical elements with diameters of 600 mm. We also present a thermography-based nondestructive quality control technique, which is key to assuring good adhesion and preventing delamination when thermal cycling. We demonstrate the average reflectance of about 2.6% (0.9%) for two observing bands centered at 90/150 (220/280) GHz. At room temperature, the average transmittance of a 105 mm square test sample at 220/280 GHz is 83%, and it will increase to 90% at 100 K, attributed to reduced absorption losses. Therefore, our developed layering technique has proved effective for 220/280 GHz applications, particularly in addressing dielectric loss concerns. This AR coating technology has been deployed in the cryogenic HWP and IR filters of the Simons Array and the Simons observatory experiments and applies to future experiments such as CMB-S4.

The diagnosis and misdiagnosis of Sjögren disease.

Although one of the most common systemic autoimmune disorders, Sjögren disease (SjD) may be overlooked in patients presenting with non-specific symptoms or no complaints of sicca symptoms. SjD is not a condition to be missed as patients could present with serious extra-glandular manifestations, including lymphomas. In this article, we discuss the diagnostic pitfalls of this disorder and encourage physicians to consider carefully the 'non-textbook' presentations.

Use of natural language understanding to facilitate surgical de-escalation of axillary staging in patients with breast cancer.

Natural language understanding (NLU) may be particularly well-equipped for enhanced data capture from the electronic health record (EHR) given its examination of both content- and context-driven extraction. We developed and applied a NLU model to examine rates of pathological node positivity (pN+) and rates of lymphedema to determine if omission of routine axillary staging could be extended to younger patients with ER+/cN0 disease. We found that rates of pN+ and arm lymphedema were similar between patients 55-69yo and ≥70yo, with rates of lymphedema exceeding rates of pN+ for clinical stage T1c and smaller disease. Data from our NLU model suggest that omission of SLNB might be extended beyond Choosing Wisely recommendations, limited to those over 70 years old, to all postmenopausal women with early-stage ER+/cN0 disease. These data support the recently-reported SOUND trial results and provide additional granularity to facilitate surgical de-escalation.

Use of Natural Language Understanding to Facilitate Surgical De-Escalation of Axillary Staging in Patients With Breast Cancer.

PURPOSE: Natural language understanding (NLU) may be particularly well equipped for enhanced data capture from the electronic health record given its examination of both content-driven and context-driven extraction. METHODS: We developed and applied a NLU model to examine rates of pathological node positivity (pN+) and rates of lymphedema to determine whether omission of routine axillary staging could be extended to younger patients with estrogen receptor-positive (ER+)/cN0 disease. RESULTS: We found that rates of pN+ and arm lymphedema were similar between patients age 55-69 years and ≥70 years, with rates of lymphedema exceeding rates of pN+ for clinical stage T1c and smaller disease. CONCLUSION: Data from our NLU model suggest that omission of sentinel lymph node biopsy might be extended beyond Choosing Wisely recommendations, limited to those older than 70 years and to all postmenopausal women with early-stage ER+/cN0 disease. These data support the recently reported SOUND trial results and provide additional granularity to facilitate surgical de-escalation.

Mutual information for detecting multi-class biomarkers when integrating multiple bulk or single-cell transcriptomic studies.

Motivation: Biomarker detection plays a pivotal role in biomedical research. Integrating omics studies from multiple cohorts can enhance statistical power, accuracy and robustness of the detection results. However, existing methods for horizontally combining omics studies are mostly designed for two-class scenarios (e.g., cases versus controls) and are not directly applicable for studies with multi-class design (e.g., samples from multiple disease subtypes, treatments, tissues, or cell types). Results: We propose a statistical framework, namely Mutual Information Concordance Analysis (MICA), to detect biomarkers with concordant multi-class expression pattern across multiple omics studies from an information theoretic perspective. Our approach first detects biomarkers with concordant multi-class patterns across partial or all of the omics studies using a global test by mutual information. A post hoc analysis is then performed for each detected biomarkers and identify studies with concordant pattern. Extensive simulations demonstrate improved accuracy and successful false discovery rate control of MICA compared to an existing MCC method. The method is then applied to two practical scenarios: four tissues of mouse metabolism-related transcriptomic studies, and three sources of estrogen treatment expression profiles. Detected biomarkers by MICA show intriguing biological insights and functional annotations. Additionally, we implemented MICA for single-cell RNA-Seq data for tumor progression biomarkers, highlighting critical roles of ribosomal function in the tumor microenvironment of triple-negative breast cancer and underscoring the potential of MICA for detecting novel therapeutic targets. Availability: https://github.com/jianzou75/MICA.

Using a linear dynamic system to measure functional connectivity from M/EEG.

Objective.Measures of functional connectivity (FC) can elucidate which cortical regions work together in order to complete a variety of behavioral tasks. This study's primary objective was to expand a previously published model of measuring FC to include multiple subjects and several regions of interest. While FC has been more extensively investigated in vision and other sensorimotor tasks, it is not as well understood in audition. The secondary objective of this study was to investigate how auditory regions are functionally connected to other cortical regions when attention is directed to different distinct auditory stimuli.Approach.This study implements a linear dynamic system (LDS) to measure the structured time-lagged dependence across several cortical regions in order to estimate their FC during a dual-stream auditory attention task.Results.The model's output shows consistent functionally connected regions across different listening conditions, indicative of an auditory attention network that engages regardless of endogenous switching of attention or different auditory cues being attended.Significance.The LDS implemented in this study implements a multivariate autoregression to infer FC across cortical regions during an auditory attention task. This study shows how a first-order autoregressive function can reliably measure functional connectivity from M/EEG data. Additionally, the study shows how auditory regions engage with the supramodal attention network outlined in the visual attention literature.

Audiovisual Speech Perception Benefits are Stable from Preschool through Adolescence.

The ability to leverage visual cues in speech perception - especially in noisy backgrounds - is well established from infancy to adulthood. Yet, the developmental trajectory of audiovisual benefits stays a topic of debate. The inconsistency in findings can be attributed to relatively small sample sizes or tasks that are not appropriate for given age groups. We designed an audiovisual speech perception task that was cognitively and linguistically age-appropriate from preschool to adolescence and recruited a large sample ( N = 161) of children (age 4-15). We found that even the youngest children show reliable speech perception benefits when provided with visual cues and that these benefits are consistent throughout development when auditory and visual signals match. Individual variability is explained by how the child experiences their speech-in-noise performance rather than the quality of the signal itself. This underscores the importance of visual speech for young children who are regularly in noisy environments like classrooms and playgrounds.

Cancer-cell derived S100A11 promotes macrophage recruitment in ER+ breast cancer.

Macrophages are pivotal in driving breast tumor development, progression, and resistance to treatment, particularly in estrogen receptor-positive (ER+) tumors, where they infiltrate the tumor microenvironment (TME) influenced by cancer cell-secreted factors. By analyzing single-cell RNA-sequencing data from 25 ER+ tumors, we elucidated interactions between cancer cells and macrophages, correlating macrophage density with epithelial cancer cell density. We identified that S100A11, a previously unexplored factor in macrophage-cancer crosstalk, predicts high macrophage density and poor outcomes in ER+ tumors. We found that recombinant S100A11 enhances macrophage infiltration and migration in a dose-dependent manner. Additionally, in 3D models, we showed that S100A11 expression levels in ER+ cancer cells predict macrophage infiltration patterns. Neutralizing S100A11 decreased macrophage recruitment, both in cancer cell lines and in a clinically relevant patient-derived organoid model, underscoring its role as a paracrine regulator of cancer-macrophage interactions in the protumorigenic TME. This study offers novel insights into the interplay between macrophages and cancer cells in ER+ breast tumors, highlighting S100A11 as a potential therapeutic target to modulate the macrophage-rich tumor microenvironment.

The Psychoneurologic Symptom Cluster and Its Association With Breast Cancer Genomic Instability.

OBJECTIVES: To phenotype the psychoneurologic (PN) symptom cluster in individuals with metastatic breast cancer and associate those phenotypes with individual characteristics and cancer genomic variables from circulating tumor DNA. SAMPLE & SETTING: This study included 201 individuals with metastatic breast cancer recruited in western Pennsylvania. METHODS & VARIABLES: A descriptive, cross-sectional design was used. Symptom data were collected via the MD Anderson Symptom Inventory, and cancer genomic data were collected via ultra-low-pass whole-genome sequencing of circulating tumor DNA from participant blood. RESULTS: Three distinct PN symptom phenotypes were described in a population with metastatic breast cancer: mild symptoms, moderate symptoms, and severe mood-related symptoms. Breast cancer TP53 deletion was significantly associated with membership in a moderate to severe symptoms phenotype (p = 0.013). IMPLICATIONS FOR NURSING: Specific cancer genomic changes associated with increased genomic instability may be predictive of PN symptoms. This finding may enable proactive treatment or reveal new therapeutic targets for symptom management.

Breast cancer cells promote osteoclast differentiation in an MRTF-dependent paracrine manner.

Bone is a frequent site for breast cancer metastasis. Conditioning of the local tumor microenvironment (TME) through crosstalk between tumor cells and bone resident cells in the metastatic niche is a major driving force for bone colonization of breast cancer cells. The vast majority of breast cancer-associated metastasis is osteolytic in nature, and RANKL-induced differentiation of bone marrow-derived macrophages to osteoclasts (OCLs) is a key requirement for osteolytic metastatic growth of cancer cells. In this study, we demonstrate that breast cancer cell-secreted factors stimulate RANKL-induced OCL differentiation of BMDMs requiring the function of Myocardin-related transcription factor (MRTF) in tumor cells. This is partly attributed to the critical role of MRTF in maintaining the basal cellular expression of connective tissue growth factor (CTGF), a pro-osteoclastogenic matricellular factor known to promote bone metastasis in human breast cancer. Supporting these in vitro findings, bioinformatics analyses of multiple human breast cancer transcriptome datasets reveal a strong positive correlation between CTGF expression and MRTF gene signature further establishing the relevance of our findings in a human disease context. By Luminex analyses, we show that MRTF depletion in breast cancer cells has a broad impact on OCL-regulatory cell-secreted factors that extends beyond CTGF. These findings, taken together with demonstration of MRTF-dependence for bone colonization breast cancer cells in vivo, suggest that MRTF inhibition could be an effective strategy to diminish OCL formation and skeletal involvement in breast cancer. In summary, this study highlights a novel tumor-extrinsic function of MRTF relevant to breast cancer metastasis.

Long-term outcomes by lobular versus ductal histology in four NSABP adjuvant breast cancer trials.

We evaluated differences in long-term outcomes of invasive lobular carcinoma (ILC) vs breast cancers of no special type (NST) treated with anthracycline-based adjuvant chemotherapy using 4 National Surgical Adjuvant Breast and Bowel Project (NSABP) randomized phase III trials (B-22, B-25, B-28, B-30). Our cohort included 11,251 patients with NST and 1,231 with ILC. Patients with ILC were older, had larger and more often estrogen receptor-positive tumors, and more positive lymph nodes. During early follow-up (0-5 years), patients with ILC had fewer recurrences (HR: 0.797; 95% confidence interval [CI] 0.685-0.929) and deaths (HR: 0.756; 95% CI 0.623-0.917). After 5 years patients with ILC had more recurrences (HR: 1.30; 95% CI 1.085-1.558) and deaths (HR: 1.044; 95% CI 0.898-1.214). Conditional probability analysis showed significant interactions between time-period and histologic type for recurrences (p < .001) and deaths (p < .001). Patients with ILC have elevated risk of late recurrence and death compared to patients with NST.

Understanding the Phase of Responsivity and Noise Sources in Frequency-Domain Multiplexed Readout of Transition Edge Sensor Bolometers.

Cosmic microwave background (CMB) experiments have deployed focal planes with O ( 10 4 ) transition edge sensor (TES) bolometers cooled to sub-Kelvin temperatures by multiplexing the readout of many TES channels onto a single pair of wires. Digital Frequency-domain Multiplexing (DfMux) is a multiplexing technique used in many CMB polarization experiments, such as the Simons Array, SPT-3 G, and EBEX. The DfMux system studied here uses LC filters with resonant frequencies ranging from 1.5 to 4.5 MHz connected to an array of TESs. Each detector has an amplitude-modulated carrier tone at the resonant frequency of its accompanying LC resonator. The signal is recovered via quadrature demodulation where the in-phase (I) component of the demodulated current is in phase with the complex admittance of the circuit and the quadrature (Q) component is orthogonal to I. Observed excess current noise in the Q component is consistent with fluctuations in the resonant frequency. This noise has been shown to be non-orthogonal to the phase of the detector's responsivity. We present a detailed analysis of the phase of responsivity of the TES and noise sources in our DfMux readout system. Further, we investigate how modifications to the TES operating resistance and bias frequency can affect the phase of noise relative to the phase of the detector responsivity, using data from Simons Array to evaluate our predictions. We find that both the phase of responsivity and phase of noise are functions of the two tuning parameters, which can be purposefully selected to maximize signal-to-noise (SNR) ratio.