Citations

Abstract Triple-negative breast cancer (TNBC) is the most aggressive breast cancer subtype with low overall survival rates and high molecular heterogeneity; therefore, few targeted therapies are available. The luminal androgen receptor (LAR) is the most consistently identified TNBC subtype, but the clinical utility has yet to be established. Here, we constructed a novel genomic classifier, LAR-Sig, that distinguishes the LAR subtype from other TNBC subtypes and provide evidence that it is a clinically distinct disease. A meta-analysis of seven TNBC datasets (n = 1086 samples) from neoadjuvant clinical trials demonstrated that LAR patients have significantly reduced response (pCR) rates than non-LAR TNBC patients (odds ratio = 2.11, 95% CI: 1.33, 2.89). Moreover, deconvolution of the tumor microenvironment confirmed an enrichment of luminal epithelium corresponding with a decrease in basal and myoepithelium in LAR TNBC tumors. Increased immunosuppression in LAR patients may lead to a decreased presence of cycling T-cells and plasma cells. While, an increased presence of myofibroblast-like cancer-associated cells may impede drug delivery and treatment. In summary, the lower levels of tumor infiltrating lymphocytes (TILs), reduced immune activity in the micro-environment, and lower pCR rates after NAC, suggest that new therapeutic strategies for the LAR TNBC subtype need to be developed.

Abstract Background: Biopsy diagnoses of benign breast disease (BBD) confer a 1.5- to 4-fold increased risk of developing breast cancer (BC) compared with women without BBD. Previously, we reported that decreased numbers of specific immune cell types in lobules of BBD biopsies predicted increased BC risk, suggesting the promise of future tissue biomarker studies to define BC risk markers among BBD patients. Thus, we applied protein-based GeoMx® Digital Spatial Profiling (DSP) to BBD biopsies preceding BC (cases) and to BBD biopsies from cancer-free patients (controls) to identify possible BC risk markers, which we then evaluated in subsequent BC tissues and in surrounding normal lobules of cases. Methods: Archived pathology slides of BBD biopsies were reviewed and used to guide preparation of TMAs containing 1.0-mm diameter FFPE cores of lobules from an age- and cohort-period-matched set of 91 cases and 88 controls from the Mayo Clinic BBD Cohort. For patients who later developed BC, we prepared TMAs of BC tissue and surrounding mapped normal lobules. We applied GeoMx® DSP (immune and canonical signaling proteins) to both sets of TMAs. Following QC and data normalization, associations of case status with log-transformed biomarker expression in lobules of BBD biopsies were carried out using linear mixed modeling approaches, accounting for multiple ROIs per individual. Biomarkers significantly associated with case status (p<0.05) were further examined in BCs and adjacent normal lobules, using similar approaches. Results: The mean age at BBD biopsy was 52 years, and at BC diagnosis of cases, 61.4 years (mean time from BBD to BC was 10.2 years). A family history of BC was more frequent among cases (70% versus 43%; chi-square p=0.002). Of 72 biomarkers tested, 46 (64.4%) were evaluable after QC and normalization and 5 were associated with BC risk after adjustment for family history of BC: BCL2 (p=0.005), STING (p=0.006), CD44 (p=0.02), S100 protein (p=0.03) and pan-AKT (p=0.05); each showed higher levels in lobules of BBD biopsies of controls than cases. Three unique patterns appeared when examining these biomarkers across tissue type within cases: for BCL2 (p=5 x 10-9) and STING (p=2 x 10-19), levels were high in both BC and lobules surrounding BC but low in preceding BBD; for GAPDH (p=4 x 10-53) and pan-AKT (p=2 x 10-33), levels were high in BC, low in preceding BBD, and moderate in lobules surrounding BC; and for CD44 (p=2 x 10-6) and S100B (p=2 x 10-49), levels were low in BC, high in lobules surrounding BC and moderate in preceding BBD. Conclusions: Using a novel TMA of lobules in combination with DSP, we preliminarily identified immune-based and PI3 kinase-related protein biomarkers in BBD biopsies associated with BC risk. In case-only analyses, these markers demonstrated complex differences between lobules in BBD biopsies, subsequent BCs and adjacent normal lobules. Citation Format: Robert Alan Vierkant, Jodi M. Carter, Stacey J. Winham, Chen Wang, Jennifer M. Kachergus, Ji Shi, Raymond M. Moore, Bryan M. McCauley, Laura M. Pacheco-Spann, E A. Thompson, Derek C. Radisky, Amy C. Degnim, Mark E. Sherman. Towards prediction of breast cancer risk in benign biopsies with high-plex GeoMx spatial protein profiling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2203.

Abstract The goal of this study was to develop a high-plex assay to simultaneously quantitate 27 established and novel breast cancer (BC)-related, immune protein and phosphoprotein biomarkers using the GeoMx® Digital Spatial Profiler (DSP). The custom assay performance was compared to standard clinical BC biomarker assays (e.g.ER, PR, HER2, Ki67) across the spectrum of BC subtypes and in multiple laboratories. Commercially available antibodies to 27 BC-related protein biomarkers, including ER, PR, HER2, Ki-67, AR, immune-related (e.g. PD-L1) and several cell cycle/proliferation markers were oligonucleotide-tagged, and verified by immunohistochemistry for performance against untagged antibodies. The tagged antibodies were combined with 3 isotype controls and 2 housekeeping proteins into a custom BC high-plex assay for DSP. Confirmation of target specificity was done on a custom tissue microarray (TMA) (Run control) composed of (un)treated cancer cell lines and normal tissues. For clinical BC samples, each biopsy had 4x600 µm regions of interest segmented into pan-Cytokeratin+ tumoral epithelium and pan-Cytokeratin- adjacent stroma segments. With targeted UV light, oligonucleotides were collected from each segment sequentially and quantitated with nCounter. Raw counts were geomean normalized for analysis. Intra-site and inter-site assay reproducibility was assessed with the TMA and serial FFPE sections from BC biopsies. Quantitation of proteins showed high reproducibility within sites (e.g. Run 1 v. Run 2, p>0.05 for all protein targets) and the high-plex custom assay showed good concordance between 3 participating laboratories. GeoMx DSP-derived protein data correlated well with orthogonal methodologies performed on a sample subset including immunohistochemistry, reverse phase protein array, DNA and RNA-sequencing. For example, DSP protein counts for p53 protein were higher in tumors with TP53 point mutation than those with truncating TP53 alterations. Expanded sample testing is underway with 120 well-characterized breast cancer biopsies of various histologies, immune subtypes (e.g. TIL scores) and clinical biomarker phenotypes (e.g. ER+/HER2-/Ki-67 high BC; ER+/HER2-/Ki-67 low BC; HER2+ BC; ER-/PR-/HER2- BC).Our preliminary data demonstrate that this custom high-plex BC assay can quantitate protein biomarkers across a wide dynamic range with high intra-lab and inter-lab reproducibility. The assay requirement of a single 5-µm tissue section facilitates complex biomarker profiling in biopsies with limited material. The custom assay alone or in combination with other targeted DSP protein modules can simultaneously interrogate the biomarker profile, immune-based and other drug target profile, and immune microenvironment of BC specimens, providing a novel approach for actionable tumor subtyping. For research use only. Not for use in diagnostic procedures. Citation Format: Christopher Corless, Amber Bridgeman, Jinho Lee, Gary Geiss, Sarah Warren, Sarah Church, Joseph M. Beechem, E Aubrey Thompson, Jodi Carter. Development of a custom high-plex GeoMx digital spatial profiler breast cancer protein biomarker assay [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3484.

Abstract Background: As a key receptor tyrosine kinase (RTK) regulating tumorigenesis, AXL is overexpressed in multiple different types of cancers. AXL can be activated by homodimerization after binding with its ligand, growth arrest specific 6 protein (Gas6) or by heterodimerization with other RTKs. AXL overexpression and/or activation have been reported to promote cancer cell proliferation, migration/invasion and to induce resistance to targeted therapy. However, the AXL downstream signaling network has not yet been fully elucidated. Methods: We employed a CRISPR-CAS9-based knockout technology to create AXL inducible knockout cells in a human triple negative breast cancer (TNBC) cell line, HCC1395. In order to explore the downstream signaling events driven by AXL, we induced AXL knockout for 72 hours and used a 6-plex TMT-labeling-based quantitative proteomic approach to characterize the protein phosphorylation alterations induced by AXL knockout. IMAC and anti-phosphotyrosine antibody (pY1000)-based phosphopeptide enrichment methods were used to comprehensively quantify the phosphoproteome changes induced by AXL knockout. Results: In this study, we identified 38,525 phospho serine/threonine sites and 1,427 phosphotyrosine sites. In response to AXL knockout, 1,581 phospho serine/threonine sites and 40 phosphotyrosine sites showed significant differences between AXL-KO and AXL-WT groups with >1.5-fold change and p<0.05 as the cutoff. Among them, 982 phosphoserine/threonine sites and 30 phosphotyrosine sites were decreased, while 599 phospho serine/threonine sites and 10 phosphotyrosine sites were increased in AXL-KO cells. Signaling pathway enrichment analysis revealed that suppressing AXL expression could inhibit multiple important cancer related signaling pathways, including cell cycle, focal adhesion, MAPK signaling pathway, p53 signaling pathway and mTOR signaling pathway. Conclusion: Quantitative analysis of the phosphoproteome driven by AXL demonstrated that AXL plays a pivotal role in regulating canonical oncogenic signaling but also signaling pathways involved in RNA processing and DNA repair. Our data also suggest that targeting AXL could suppress these oncogenic signaling pathways and have therapeutic potential to improve clinical outcomes for patients with AXL overexpression. Citation Format: Li Wang, Nicole Pearson, Yuning Xiong, Santosh Renuse, Ran Cheng, Jodi M. Carter, Akhilesh Pandey, Xinyan Wu. Quantitative phosphoproteomic analysis of AXL signaling network in breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3913.

Objective The aim of this study was to evaluate the clinical impact of a higher spatial resolution, full field-of-view investigational photon-counting detector computed tomography (PCD-CT) on radiologist confidence in imaging findings and diagnosis of usual interstitial pneumonia (UIP) compared with conventional energy-integrating detector CT (EID-CT). Materials and Methods Patients suspected of interstitial lung disease were scanned on a PCD-CT system after informed consent and a clinically indicated EID-CT. In 2 sessions, 3 thoracic radiologists blinded to clinical history and scanner type evaluated CT images of the right and left lungs separately on EID- or PCD-CT, reviewing each lung once/session, rating confidence in imaging findings of reticulation, traction bronchiectasis, honeycombing, ground-glass opacities (GGOs), mosaic pattern, and lower lobe predominance (100-point scale: 0–33, likely absent; 34–66, indeterminate; 67–100, likely present). Radiologists also rated confidence for the probability of UIP (0–20, normal; 21–40, inconsistent with UIP; 41–60, indeterminate UIP; 61–81; probable UIP; 81–100, definite UIP) and graded image quality. Because a confidence scale of 50 represented completely equivocal findings, magnitude score (the absolute value of confidence scores from 50) was used for analysis (higher scores were more confident). Image noise was measured for each modality. The magnitude score was compared using linear mixed effects regression. The consistency of findings and diagnosis between 2 scanners were evaluated using McNemar test and weighted κ statistics, respectively. Results A total of 30 patients (mean age, 68.8 ± 11.0 years; M:F = 18:12) underwent conventional EID-CT (median CTDIvol, 7.88 mGy) and research PCD-CT (median CTDIvol, 6.49 mGy). The magnitude scores in PCD-CT were significantly higher than EID-CT for imaging findings of reticulation (40.7 vs 38.3; P = 0.023), GGO (34.4 vs 31.7; P = 0.019), and mosaic pattern (38.6 vs 35.9; P = 0.013), but not for other imaging findings (P ≥ 0.130) or confidence in UIP (34.1 vs 22.2; P < 0.059). Magnitude score of probability of UIP in PCD-CT was significantly higher than EID-CT in one reader (26.0 vs 21.5; P = 0.009). Photon-counting detector CT demonstrated a decreased number of indeterminate GGO (17 vs 26), an increased number of unlikely GGO (74 vs 50), and an increased number of likely reticulations (140 vs 130) relative to EID-CT. Interobserver agreements among 3 readers for imaging findings and probability of UIP were similar between PCD-CT and EID-CT (intraclass coefficient: 0.507–0.818 vs 0.601–0.848). Photon-counting detector CT had higher scores in overall image quality (4.84 ± 0.38) than those in EID-CT (4.02 ± 0.40; P < 0.001) despite increased image noise (mean 85.5 vs 36.1 HU). Conclusions Photon-counting detector CT provided better image quality and improved the reader confidence for presence or absence of imaging findings of reticulation, GGO, and mosaic pattern with idiosyncratic improvement in confidence in UIP presence.

AbstractAchalasia is rare in the pediatric population and should prompt clinicians to consider genetic disorders associated with this condition. While AAA syndrome (also known as Allgrove or Triple A syndrome) is commonly considered, GMPPA‐congenital disorder of glycosylation (CDG) should also be in the differential diagnosis. We report a 9‐month‐old female born to nonconsanguineous parents with achalasia and alacrima found to have two novel compound heterozygous variants in the GMPPA gene associated with GMPPA‐CDG. This rare disorder is commonly associated with developmental delay and intellectual disability. We discuss management of this disorder including the importance of confirming a genetic diagnosis and summarize reported cases.

Abstract Aims Some artificial intelligence models applied in medical practice require ongoing retraining, introduce unintended racial bias, or have variable performance among different subgroups of patients. We assessed the real-world performance of the artificial intelligence-enhanced electrocardiogram to detect left ventricular systolic dysfunction with respect to multiple patient and electrocardiogram variables to determine the algorithm’s long-term efficacy and potential bias in the absence of retraining. Methods and results Electrocardiograms acquired in 2019 at Mayo Clinic in Minnesota, Arizona, and Florida with an echocardiogram performed within 14 days were analyzed (n = 44 986 unique patients). The area under the curve (AUC) was calculated to evaluate performance of the algorithm among age groups, racial and ethnic groups, patient encounter location, electrocardiogram features, and over time. The artificial intelligence-enhanced electrocardiogram to detect left ventricular systolic dysfunction had an AUC of 0.903 for the total cohort. Time series analysis of the model validated its temporal stability. Areas under the curve were similar for all racial and ethnic groups (0.90–0.92) with minimal performance difference between sexes. Patients with a ‘normal sinus rhythm’ electrocardiogram (n = 37 047) exhibited an AUC of 0.91. All other electrocardiogram features had areas under the curve between 0.79 and 0.91, with the lowest performance occurring in the left bundle branch block group (0.79). Conclusion The artificial intelligence-enhanced electrocardiogram to detect left ventricular systolic dysfunction is stable over time in the absence of retraining and robust with respect to multiple variables including time, patient race, and electrocardiogram features.

Importance The rise of novel, more infectious SARS-CoV-2 variants has made clear the need to rapidly deploy large-scale testing for COVID-19 to protect public health. However, testing remains limited due to shortages of personal protective equipment (PPE), naso- and oropharyngeal swabs, and healthcare workers. Simple test methods are needed to enhance COVID-19 screening. Here, we describe a simple, and inexpensive spit-test for COVID-19 screening called Patient Self-Collection of Sample-CoV2 (PSCS-CoV2). Objective To evaluate an affordable and convenient test for COVID-19. Methods The collection method relies on deep throat sputum (DTS) self-collected by the subject without the use of swabs, and was hence termed the Self-Collection of Sample for SARS-CoV-2 (abbreviated PSCS-CoV2). We used a phenol-chloroform extraction method for the viral RNA. We then tested for SARS-CoV-2 using real-time reverse transcription polymerase chain reaction with primers against at least two coding regions of the viral nucleocapsid protein (N1 and N2 or E) of SARS-CoV-2. We evaluted the sensitivity and specificity of our protocol. In addition we assess the limit of detection, and efficacy of our Viral Inactivating Solution. We also evaluated our protocol, and pooling strategy from volunteers on a local college campus. Results We show that the PSCS-CoV2 method accurately identified 42 confirmed COVID-19 positives, which were confirmed through the nasopharyngeal swabbing method of an FDA approved testing facility. For samples negative for COVID-19, we show that the cycle threshold for N1, N2, and RP are similar between the PSCS-CoV2 and nasopharynx swab collection method (n = 30). We found a sensitivity of 100% (95% Confidence Interval [CI], 92-100) and specifity of 100% (95% CI, 89-100) for our PSCS-CoV2 method. We determined our protocol has a limit of detection of 1/10,000 for DTS from a COVID-19 patient. In addition, we show field data of the PSCS-CoV2 method on a college campus. Ten of the twelve volunteers (N1 < 30) that we tested as positive were subsequently tested positive by an independent laboratory. Finally, we show proof of concept of a pooling strategy to test for COVID-19, and recommend pool sizes of four if the positivity rate is less than 15%. Conclusion and Relevance We developed a DTS-based protocol for COVID-19 testing with high sensitivity and specificity. This protocol can be used by non-debilitated adults without the assistance of another adult, or by non-debilitated children with the assistance of a parent or guardian. We also discuss pooling strategies based on estimated positivity rates to help conserve resources, time, and increase throughput. The PSCS-CoV2 method can be a key component of community-wide efforts to slow the spread of COVID-19.

Abstract Aims Artificial intelligence (AI) enabled electrocardiography (ECG) can detect latent atrial fibrillation (AF) in patients with sinus rhythm (SR). However, the change of AI-ECG probability before and after the first AF episode is not well characterized. We sought to characterize the temporal trend of AI-ECG AF probability around the first episode of AF. Methods and results We retrospectively studied adults who had at least one ECG in SR prior to an ECG that documented AF. An AI network calculated the AF probability from ECGs during SR (positive defined &gt;8.7%, based on optimal sensitivity and specificity). The AI-ECG probability was reported prior to and after the first episode of AF and stratified by age and CHA2DS2-VASc score. Mixed effect models were used to assess the rate of change between time points. A total of 59 212 patients with 544 330 ECGs prior to AF and 413 486 ECGs after AF were included. The mean time between the first positive AI-ECG and first AF was 5.4 ± 5.7 years. The mean AI-ECG probability was 19.8% 2–5 years prior to AF, 23.6% 1–2 years prior to AF, 34.0% 0–3 months prior to AF, 40.9% 0–3 months after AF, 35.2% 1–2 years after AF, and 42.2% 2–5 years after AF (P &lt; 0.001). The rate of increase prior to AF was higher for age &gt;50 years CHA2DS2-VASc score ≥4. Conclusion The AI-ECG probability progressively increases with time prior to the first AF episode, transiently decreases 1–2 years following AF and continues to increase thereafter.