Predicting nodal metastases in squamous cell carcinoma of the oral tongue using artificial intelligence.

OBJECTIVE: The presence of occult nodal metastases in patients with squamous cell carcinoma (SCC) of the oral tongue has implications for treatment. Upwards of 30% of patients will have occult nodal metastases, yet a significant number of patients undergo unnecessary neck dissection to confirm nodal status. This study sought to predict the presence of nodal metastases in patients with SCC of the oral tongue using a convolutional neural network (CNN) that analyzed visual histopathology from the primary tumor alone. METHODS: Cases of SCC of the oral tongue were identified from the records of a single institution. Only patients with complete pathology data were included in the study. The primary tumors were randomized into 2 groups for training and testing, which was performed at 2 different levels of supervision. Board-certified pathologists annotated each slide. HALO-AI convolutional neural network and image software was used to perform training and testing. Receiver operator characteristic (ROC) curves and the Youden J statistic were used for primary analysis. RESULTS: Eighty-nine cases of SCC of the oral tongue were included in the study. The best performing algorithm had a high level of supervision and a sensitivity of 65% and specificity of 86% when identifying nodal metastases. The area under the curve (AUC) of the ROC curve for this algorithm was 0.729. CONCLUSION: A CNN can produce an algorithm that is able to predict nodal metastases in patients with squamous cell carcinoma of the oral tongue by analyzing the visual histopathology of the primary tumor alone.

Helping, Sharing, and Comforting in Young Children: Links to Individual Differences in Attachment.

Although attachment theory has long posited a link between early experiences of care and children's prosocial behavior, investigations of this association have not embraced the multifaceted nature of prosociality. This study is the first to assess associations between child attachment and independent observations of helping, sharing, and comforting. Attachment quality in 3- to 5-year-old children (N = 137) was linked to all three prosocial behaviors. Additionally, bifactor analyses revealed distinct associations between attachment and children's general prosocial dispositions and their specific abilities to meet the unique challenges of helping and, marginally, comforting. These findings underscore the importance of considering multiple explanations for links between attachment and prosocial behavior and provide novel insights into sources of variation in children's prosociality.

Circle of Security-Parenting: A randomized controlled trial in Head Start.

Although evidence shows that attachment insecurity and disorganization increase risk for the development of psychopathology (Fearon, Bakermans-Kranenburg, van IJzendoorn, Lapsley, & Roisman, 2010; Groh, Roisman, van IJzendoorn, Bakermans-Kranenburg, & Fearon, 2012), implementation challenges have precluded dissemination of attachment interventions on the broad scale at which they are needed. The Circle of Security-Parenting Intervention (COS-P; Cooper, Hoffman, & Powell, 2009), designed with broad implementation in mind, addresses this gap by training community service providers to use a manualized, video-based program to help caregivers provide a secure base and a safe haven for their children. The present study is a randomized controlled trial of COS-P in a low-income sample of Head Start enrolled children and their mothers. Mothers (N = 141; 75 intervention, 66 waitlist control) completed a baseline assessment and returned with their children after the 10-week intervention for the outcome assessment, which included the Strange Situation. Intent to treat analyses revealed a main effect for maternal response to child distress, with mothers assigned to COS-P reporting fewer unsupportive (but not more supportive) responses to distress than control group mothers, and a main effect for one dimension of child executive functioning (inhibitory control but not cognitive flexibility when maternal age and marital status were controlled), with intervention group children showing greater control. There were, however, no main effects of intervention for child attachment or behavior problems. Exploratory follow-up analyses suggested intervention effects were moderated by maternal attachment style or depressive symptoms, with moderated intervention effects emerging for child attachment security and disorganization, but not avoidance; for inhibitory control but not cognitive flexibility; and for child internalizing but not externalizing behavior problems. This initial randomized controlled trial of the efficacy of COS-P sets the stage for further exploration of "what works for whom" in attachment intervention.

Myeloid NCOA4 sequesters KEAP1 to reduce ferroptosis for protection against salmonellosis in mice.

Salmonellosis, caused by Salmonella enterica serovar Typhimurium, is a significant global threat. Host immunity limits bacterial replication by inducing hepcidin, which degrades ferroportin, reducing iron transfer. However, this boosts macrophage iron storage, aiding intracellular pathogens like Salmonella. Mice lacking ferritin heavy chain (FTH1) in myeloid cells suffer worsened Salmonella infection. Nuclear receptor co-activator 4 (NCOA4) regulates iron release via FTH1 degradation during low iron, but its role in salmonellosis is unclear. Here, we reveal that myeloid NCOA4 deficiency augments spleen iron levels and increases cellular iron accumulation, oxidative stress, and ferroptosis in bone marrow-derived macrophages. This deficiency also increases susceptibility to Salmonella-induced colitis in mice. Mechanistically, NCOA4 suppresses oxidative stress by directly binding to the E3 ubiquitin ligase Kelch-like ECH-associated protein 1 (KEAP1) and stabilizing the antioxidant transcription factor nuclear factor-erythroid 2-related factor 2 (NRF2). Activation of NRF2 protects myeloid NCOA4 knockout mice from Salmonella-induced colitis. Antioxidant Tempol and myeloid cell-targeted curcumin offer protection against colitis in myeloid NCOA4-deficient mice. A low iron diet and ferroptosis inhibition also mitigate the heightened colitis in these mice. Overexpression of myeloid cell-specific NCOA4 confers protection against Salmonella-induced colitis via upregulating NRF2 signaling. Serum iron was reduced in myeloid NCOA4-overexpressing mice, but not in NCOA4-deficient mice. Targeted serum metabolomics analysis revealed that many lipids were decreased in myeloid NCOA4-deficient mice, while several of them were increased in myeloid NCOA4-overexpressing mice. Together, this study not only advances our understanding of NCOA4/KEAP1/NRF2/ferroptosis axis but also paves the way for novel myeloid cell-targeted therapies to combat salmonellosis.

Myeloid FTH1 Deficiency Protects Mice From Colitis and Colitis-associated Colorectal Cancer via Reducing DMT1-Imported Iron and STAT3 Activation.

BACKGROUND: Myeloid cells are critical for iron and immune homeostasis. Ferritin heavy chain (FTH1) is essential for intracellular iron storage. Myeloid FTH1 is important in the pathogenesis of many inflammatory diseases. However, the role of myeloid FTH1 in colitis and colitis-associated cancer has not been determined. METHODS: Myeloid FTH1 deficient and wild-type mice were treated with dextran sodium sulfate (DSS) or azoxymethane (AOM)-DSS to compare their susceptibility to acute colitis or colitis-associated cancer. RESULTS: Myeloid FTH1-deficient mice fed with a high-iron diet were less susceptible to DSS-induced acute colitis than wild type mice. Mechanistic studies showed that myeloid FTH1 deficiency resulted in lower expression of an iron uptake protein divalent metal transporter 1 (DMT1) and active phosphorylated signal transducer and activator of transcription 3 (STAT3) in the colon tissues. Our studies also showed that pharmacological STAT3 reactivation restored the susceptibility of myeloid FTH1-deficient mice to DSS-induced acute colitis. Consistently, myeloid FTH1-deficient mice fed with a high-iron diet had reduced DMT1, phosphorylated STAT3 and inflammation in their colon tissues, and were less susceptible to colitis-associated colorectal cancer. CONCLUSIONS: Our study demonstrated that myeloid FTH1 is required for colitis and colitis-associated colorectal cancer via maintaining of DMT1-iron-STAT3 signaling activation under excess iron condition.

Iron promotes glycolysis to drive colon tumorigenesis.

Colorectal cancer (CRC) is the third most common cancer and is also the third leading cause of cancer-related death in the USA. Understanding the mechanisms of growth and progression of CRC is essential to improve treatment. Macronutrients such as glucose are energy source for a cell. Many tumor cells exhibit increased aerobic glycolysis. Increased tissue micronutrient iron levels in both mice and humans are also associated with increased colon tumorigenesis. However, if iron drives colon carcinogenesis via affecting glucose metabolism is still not clear. Here we found the intracellular glucose levels in tumor colonoids were significantly increased after iron treatment. 13C-labeled glucose flux analysis indicated that the levels of several labeled glycolytic products were significantly increased, whereas several tricarboxylic acid cycle intermediates were significantly decreased in colonoids after iron treatment. Mechanistic studies showed that iron upregulated the expression of glucose transporter 1 (GLUT1) and mediated an inhibition of the pyruvate dehydrogenase (PDH) complex function via directly binding with tankyrase and/or pyruvate dehydrogenase kinase (PDHK) 3. Pharmacological inhibition of GLUT1 or PDHK reactivated PDH complex function and reduced high iron diet-enhanced tumor formation. In conclusion, excess iron promotes glycolysis and colon tumor growth at least partly through the inhibition of the PDH complex function.

Lymph Node Metastases in Papillary Thyroid Carcinoma can be Predicted by a Convolutional Neural Network: a Multi-Institution Study.

OBJECTIVES: The presence of nodal metastases in patients with papillary thyroid carcinoma (PTC) has both staging and treatment implications. However, lymph nodes are often not removed during thyroidectomy. Prior work has demonstrated the capability of artificial intelligence (AI) to predict the presence of nodal metastases in PTC based on the primary tumor histopathology alone. This study aimed to replicate these results with multi-institutional data. METHODS: Cases of conventional PTC were identified from the records of 2 large academic institutions. Only patients with complete pathology data, including at least 3 sampled lymph nodes, were included in the study. Tumors were designated "positive" if they had at least 5 positive lymph node metastases. First, algorithms were trained separately on each institution's data and tested independently on the other institution's data. Then, the data sets were combined and new algorithms were developed and tested. The primary tumors were randomized into 2 groups, one to train the algorithm and another to test it. A low level of supervision was used to train the algorithm. Board-certified pathologists annotated the slides. HALO-AI convolutional neural network and image software was used to perform training and testing. Receiver operator characteristic curves and the Youden J statistic were used for primary analysis. RESULTS: There were 420 cases used in analyses, 45% of which were negative. The best performing single institution algorithm had an area under the curve (AUC) of 0.64 with a sensitivity and specificity of 65% and 61% respectively, when tested on the other institution's data. The best performing combined institution algorithm had an AUC of 0.84 with a sensitivity and specificity of 68% and 91% respectively. CONCLUSION: A convolutional neural network can produce an accurate and robust algorithm that is capable of predicting nodal metastases from primary PTC histopathology alone even in the setting of multi-institutional data.

Transferrin Receptor-Mediated Iron Uptake Promotes Colon Tumorigenesis.

Transferrin receptor (TFRC) is the major mediator for iron entry into a cell. Under excessive iron conditions, TFRC is expected to be reduced to lower iron uptake and toxicity. However, the mechanism whereby TFRC expression is maintained at high levels in iron-enriched cancer cells and the contribution of TFRC to cancer development are enigmatic. Here the work shows TFRC is induced by adenomatous polyposis coli (APC) gene loss-driven ß-catenin activation in colorectal cancer, whereas TFRC-mediated intratumoral iron accumulation potentiates ß-catenin signaling by directly enhancing the activity of tankyrase. Disruption of TFRC leads to a reduction of colonic iron levels and iron-dependent tankyrase activity, which caused stabilization of axis inhibition protein 2 (AXIN2) and subsequent repression of the ß-catenin/c-Myc/E2F Transcription Factor 1/DNA polymerase delta1 (POLD1) axis. POLD1 knockdown, iron chelation, and TFRC disruption increase DNA replication stress, DNA damage response, apoptosis, and reduce colon tumor growth. Importantly, a combination of iron chelators and DNA damaging agents increases DNA damage response and reduces colon tumor cell growth. TFRC-mediated iron import is at the center of a novel feed-forward loop that facilitates colonic epithelial cell survival. This discovery may provide novel strategies for colorectal cancer therapy.

A Deep Learning Convolutional Neural Network Can Differentiate Between Helicobacter Pylori Gastritis and Autoimmune Gastritis With Results Comparable to Gastrointestinal Pathologists.

CONTEXT.­: Pathology studies using convolutional neural networks (CNNs) have focused on neoplasms, while studies in inflammatory pathology are rare. We previously demonstrated a CNN that differentiates reactive gastropathy, Helicobacter pylori gastritis (HPG), and normal gastric mucosa. OBJECTIVE.­: To determine whether a CNN can differentiate the following 2 gastric inflammatory patterns: autoimmune gastritis (AG) and HPG. DESIGN.­: Gold standard diagnoses were blindly established by 2 gastrointestinal (GI) pathologists. One hundred eighty-seven cases were scanned for analysis by HALO-AI. All levels and tissue fragments per slide were included for analysis. The cases were randomized, 112 (60%; 60 HPG, 52 AG) in the training set and 75 (40%; 40 HPG, 35 AG) in the test set. A HALO-AI correct area distribution (AD) cutoff of 50% or more was required to credit the CNN with the correct diagnosis. The test set was blindly reviewed by pathologists with different levels of GI pathology expertise as follows: 2 GI pathologists, 2 general surgical pathologists, and 2 residents. Each pathologist rendered their preferred diagnosis, HPG or AG. RESULTS.­: At the HALO-AI AD percentage cutoff of 50% or more, the CNN results were 100% concordant with the gold standard diagnoses. On average, autoimmune gastritis cases had 84.7% HALO-AI autoimmune gastritis AD and HP cases had 87.3% HALO-AI HP AD. The GI pathologists, general anatomic pathologists, and residents were on average, 100%, 86%, and 57% concordant with the gold standard diagnoses, respectively. CONCLUSIONS.­: A CNN can distinguish between cases of HPG and autoimmune gastritis with accuracy equal to GI pathologists.

Quantitative image analysis in the assessment of diffuse large B-cell lymphoma.

Proliferation rates in diffuse large B-cell lymphoma have been associated with conflicting outcomes in the literature, more often with high proliferation associated with poor prognosis. In most studies, the proliferation rate was estimated by a pathologist using an immunohistochemical stain for the monoclonal antibody Ki-67. We hypothesized that a quantitative image analysis algorithm would give a more accurate estimate of the proliferation rate, leading to better associations with survival. In all, 84 cases of diffuse large B-cell lymphoma were selected according to the World Health Organization criteria. Ki-67 percentage positivity estimated by the pathologist was recorded from the original report. The same slides were then scanned using an Aperio ImageScope, and Ki-67 percentage positivity was calculated using a computer-based quantitative immunohistochemistry nuclear algorithm. In addition, chart review was performed and survival time was recorded. The Ki-67 percentage estimated by the pathologist from the original report versus quantitative image analysis was significantly correlated (P<0.001), but pathologist Ki-67 percentages were significantly higher than quantitative image analysis (P=0.021). There was less agreement at lower Ki-67 percentages. Comparison of Ki-67 percentage positivity versus survival did not show significant association either with pathologist estimate or quantitative image analysis. However, although not significant, there was a trend of worse survival at higher proliferation rates detected by the pathologist but not by quantitative image analysis. Interestingly, our data suggest that the Ki-67 percentage positivity as assessed by the pathologist may be more closely associated with survival outcome than that identified by quantitative image analysis. This may indicate that pathologists are better at selecting appropriate areas of the slide. More cases are needed to assess whether this finding would be statistically significant. Due to the good correlation between pathologist estimate and quantitative image analysis, there is no substantial benefit to using quantitative image analysis at this point of time.

Mitophagy protein PINK1 suppresses colon tumor growth by metabolic reprogramming via p53 activation and reducing acetyl-CoA production.

Colorectal cancer (CRC) is the third leading cause of cancer-related deaths in the US. Understanding the mechanisms of CRC progression is essential to improve treatment. Mitochondria is the powerhouse for healthy cells. However, in tumor cells, less energy is produced by the mitochondria and metabolic reprogramming is an early hallmark of cancer. The metabolic differences between normal and cancer cells are being interrogated to uncover new therapeutic approaches. Mitochondria targeting PTEN-induced kinase 1 (PINK1) is a key regulator of mitophagy, the selective elimination of damaged mitochondria by autophagy. Defective mitophagy is increasingly associated with various diseases including CRC. However, a significant gap exists in our understanding of how PINK1-dependent mitophagy participates in the metabolic regulation of CRC. By mining Oncomine, we found that PINK1 expression was downregulated in human CRC tissues compared to normal colons. Moreover, disruption of PINK1 increased colon tumorigenesis in two colitis-associated CRC mouse models, suggesting that PINK1 functions as a tumor suppressor in CRC. PINK1 overexpression in murine colon tumor cells promoted mitophagy, decreased glycolysis and increased mitochondrial respiration potentially via activation of p53 signaling pathways. In contrast, PINK1 deletion decreased apoptosis, increased glycolysis, and reduced mitochondrial respiration and p53 signaling. Interestingly, PINK1 overexpression in vivo increased apoptotic cell death and suppressed colon tumor xenograft growth. Metabolomic analysis revealed that acetyl-CoA was significantly reduced in tumors with PINK1 overexpression, which was partly due to activation of the HIF-1α-pyruvate dehydrogenase (PDH) kinase 1 (PDHK1)-PDHE1α axis. Strikingly, treating mice with acetate increased acetyl-CoA levels and rescued PINK1-suppressed tumor growth. Importantly, PINK1 disruption simultaneously increased xenografted tumor growth and acetyl-CoA production. In conclusion, mitophagy protein PINK1 suppresses colon tumor growth by metabolic reprogramming and reducing acetyl-CoA production.

Predicting nodal metastases in papillary thyroid carcinoma using artificial intelligence.

BACKGROUND: The presence of nodal metastases is important in the treatment of papillary thyroid carcinoma (PTC). We present our experience using a convolutional neural network (CNN) to predict the presence of nodal metastases in a series of PTC patients using visual histopathology from the primary tumor alone. METHODS: 174 cases of PTC were evaluated for the presence or absence of lymph metastases. The artificial intelligence (AI) algorithm was trained and tested on its ability to discern between the two groups. RESULTS: The best performing AI algorithm demonstrated a sensitivity and specificity of 94% and 100%, respectively, when identifying nodal metastases. CONCLUSION: A CNN can be used to accurately predict the likelihood of nodal metastases in PTC using visual data from the primary tumor alone.

The efficacy of blood culture in postoperative patients.

Blood cultures are often obtained in postoperative patients to rule out bloodstream infections. Our study objectives were to determine the efficacy of blood cultures in postoperative patients with suspected sepsis and to determine variables predisposing patients to positive cultures. This was a retrospective study including patients with blood cultures drawn from January to March 2009 at our institution. We recorded demographics, presence of fever (temperature 101.5 degrees F or higher), elevated white blood cell count (12,000/microL or greater), central line, diabetes, intensive care unit admission, postoperative day of blood draw, National Research Council surgical wound classification, and pre- or postoperative antibiotics. Blood cultures were drawn from 150 patients undergoing surgery within 30 days prior. Sixteen had positive cultures and nine were true-positives (6.3%). There was no statistical difference (P > 0.05) between patients with positive and negative cultures except that those with negative cultures were more likely to have received preoperative antibiotics (P = 0.0186). Blood cultures are invasive, expensive tests with low yield. We recommend that blood cultures be drawn in patients not receiving preoperative antibiotics who have undergone surgery more than 4 days before culture.

A Deep Learning Convolutional Neural Network Can Recognize Common Patterns of Injury in Gastric Pathology.

CONTEXT.­: Most deep learning (DL) studies have focused on neoplastic pathology, with the realm of inflammatory pathology remaining largely untouched. OBJECTIVE.­: To investigate the use of DL for nonneoplastic gastric biopsies. DESIGN.­: Gold standard diagnoses were blindly established by 2 gastrointestinal pathologists. For phase 1, 300 classic cases (100 normal, 100 Helicobacter pylori, 100 reactive gastropathy) that best displayed the desired pathology were scanned and annotated for DL analysis. A total of 70% of the cases for each group were selected for the training set, and 30% were included in the test set. The software assigned colored labels to the test biopsies, which corresponded to the area of the tissue assigned a diagnosis by the DL algorithm, termed area distribution (AD). For Phase 2, an additional 106 consecutive nonclassical gastric biopsies from our archives were tested in the same fashion. RESULTS.­: For Phase 1, receiver operating curves showed near perfect agreement with the gold standard diagnoses at an AD percentage cutoff of 50% for normal (area under the curve [AUC] = 99.7%) and H pylori (AUC = 100%), and 40% for reactive gastropathy (AUC = 99.9%). Sensitivity/specificity pairings were as follows: normal (96.7%, 86.7%), H pylori (100%, 98.3%), and reactive gastropathy (96.7%, 96.7%). For phase 2, receiver operating curves were slightly less discriminatory, with optimal AD cutoffs reduced to 40% across diagnostic groups. The AUCs were 91.9% for normal, 100% for H pylori, and 94.0% for reactive gastropathy. Sensitivity/specificity parings were as follows: normal (73.7%, 79.6%), H pylori (95.7%, 100%), reactive gastropathy (100%, 62.5%). CONCLUSIONS.­: A convolutional neural network can serve as an effective screening tool/diagnostic aid for H pylori gastritis.

Comparing Deep Learning and Immunohistochemistry in Determining the Site of Origin for Well-Differentiated Neuroendocrine Tumors.

BACKGROUND: Determining the site of origin for metastatic well-differentiated neuroendocrine tumors (WDNETs) is challenging, and immunohistochemical (IHC) profiles do not always lead to a definitive diagnosis. We sought to determine if a deep-learning convolutional neural network (CNN) could improve upon established IHC profiles in predicting the site of origin in a cohort of WDNETs from the common primary sites. MATERIALS AND METHODS: Hematoxylin and eosin (H&E)-stained tissue microarrays (TMAs) were created using 215 WDNETs arising from the known primary sites. A CNN trained and tested on 60% (n = 130) and 40% (n = 85) of these cases, respectively. One hundred and seventy-nine cases had TMA tissue remaining for the IHC analysis. These cases were stained with IHC markers pPAX8, CDX2, SATB2, and thyroid transcription factor-1 (markers of pancreas/duodenum, ileum/jejunum/duodenum, colorectum/appendix, and lung WDNET sites of origin, respectively). The CNN diagnosis was deemed correct if it designated a majority or plurality of the tumor area as the known site of origin. The IHC diagnosis was deemed correct if the most specific marker for a particular site of origin met an H-score threshold determined by two pathologists. RESULTS: When all cases were considered, the CNN correctly identified the site of origin at a lower rate compared to IHC (72% vs. 82%, respectively). Of the 85 cases in the CNN test set, 66 had sufficient TMA material for IHC stains, thus 66 cases were available for a direct case-by-case comparison of IHC versus CNN. The CNN correctly identified 70% of these cases, while IHC correctly identified 76%, a finding that was not statistically significant (P = 0.56). CONCLUSION: A CNN can identify WDNET site of origin at an accuracy rate close to the current gold standard IHC methods.

Digital Whole Slide Imaging Compared With Light Microscopy for Primary Diagnosis in Surgical Pathology.

CONTEXT.­: The adoption of digital capture of pathology slides as whole slide images (WSI) for educational and research applications has proven utility. OBJECTIVE.­: To compare pathologists' primary diagnoses derived from WSI versus the standard microscope. Because WSIs differ in format and method of observation compared with the current standard glass slide microscopy, this study is critical to potential clinical adoption of digital pathology. DESIGN.­: The study enrolled a total of 2045 cases enriched for more difficult diagnostic categories and represented as 5849 slides were curated and provided for diagnosis by a team of 19 reading pathologists separately as WSI or as glass slides viewed by light microscope. Cases were reviewed by each pathologist in both modalities in randomized order with a minimum 31-day washout between modality reads for each case. Each diagnosis was compared with the original clinical reference diagnosis by an independent central adjudication review. RESULTS.­: The overall major discrepancy rates were 3.64% for WSI review and 3.20% for manual slide review diagnosis methods, a difference of 0.44% (95% CI, -0.15 to 1.03). The time to review a case averaged 5.20 minutes for WSI and 4.95 minutes for glass slides. There was no specific subset of diagnostic category that showed higher rates of modality-specific discrepancy, though some categories showed greater discrepancy than others in both modalities. CONCLUSIONS.­: WSIs are noninferior to traditional glass slides for primary diagnosis in anatomic pathology.

A Comprehensive Evaluation of Special AT-rich Sequence-binding Protein 2 (SATB2) Immunohistochemical Staining in Mucinous Tumors From Gastrointestinal and Nongastrointestinal Sites.

Special AT-rich sequence-binding protein 2 (SATB2) is an accurate marker for conventional colorectal carcinoma (CRC), although its sensitivity and specificity in mucinous tumors from the colon and other sites remains unknown. The objective of this study is to evaluate the accuracy of SATB2 expression detected by immunohistochemical assay, as a marker of primary CRC in mucinous adenocarcinomas. SATB2 immunohistochemical stains were performed on whole sections from 63 conventional CRCs (controls), 47 mucinous CRCs (mCRC), and 182 noncolorectal mucinous tumors. SATB2 intensity was scored as 1 to 3 based on the estrogen receptor/progesterone receptor grading system, and the percent positive cells was scored in broad categories as follows: 0 (negative)≤5%, 1=5% to 49%, 2≥50%. An optimal sensitivity/specificity pairing (83% and 95%, respectively) was achieved in the mCRCs when the additive intensity and percent score was ≥3 (ie, intensity score+percent score=total score). Defining this total score (histologic score/"H score") as a "positive" result, the sensitivity of SATB2 for conventional CRC was 98% (62/63) versus 83% (39/47) for mCRCs (P=0.02); whereas 5% (9/182) of all noncolorectal mucinous tumors were considered positive. SATB2 especially demonstrated reduced specificity when applied to mucinous gastroesophageal and breast carcinomas, which showed significant expression in 27% and 9% of cases, respectively. In summary, SATB2 is a less sensitive marker of colorectal origin in mCRC compared with conventional CRC and shows significantly reduced specificity in mucinous gastroesophageal and breast primaries.

SATB2 Versus CDX2: A Battle Royale for Diagnostic Supremacy in Mucinous Tumors.

CONTEXT.­: Metastatic mucinous tumors present a diagnostic challenge for pathologists as tumor histomorphology is often nonspecific and optimal immunoprofiles are still under investigation. OBJECTIVE.­: To present a head-to-head comparison of special AT-rich sequence-binding protein 2 (SATB2) and caudal type homeobox 2 (CDX2) expression in a diverse array of primary mucinous tumors. DESIGN.­: SATB2 and CDX2 immunohistochemical stains were performed on whole sections from 44 mucinous colorectal carcinomas and 175 noncolorectal mucinous tumors. A nuclear scoring system measuring intensity (0-3+) and percentage staining (0 = <5%, 1 = 5%-49%, 2 = ≥50%) was implemented, producing an additive histologic score (H-score). RESULTS.­: SATB2 demonstrated acceptable accuracy at low to moderate expression levels (H-scores of 1-4). With these H-score cutoffs, overall accuracy was greater than 90%. In contrast, CDX2's accuracy rivaled that of SATB2 only at an H-score of 5 (89.0%), as its specificity suffered at lower expression levels (<70.0% at H-scores of 1-4). Using a moderate H-score cutoff of 3 or higher, significant differences for both sensitivity and specificity were identified between SATB2 and CDX2 (P = .01 for sensitivity and P < .001 for specificity), though these stains were near equivalent when each was interpreted as positive at its respective optimal H-score (SATB2 ≥ 3 and CDX2 = 5). CONCLUSIONS.­: SATB2 is a more accurate marker of colorectal origin across a variety of expression levels compared with CDX2 when applied to mucinous tumors from a host of primary sites. However, these stains are near equivalent when each is interpreted at its optimal expression level.

Tumor Heterogeneity as a Predictor of Response to Neoadjuvant Chemotherapy in Locally Advanced Rectal Cancer.

BACKGROUND: Neoadjuvant chemoradiotherapy (nCRT) is the standard of care for locally advanced adenocarcinoma of the rectum, but it is currently unknown which patients have disease that will respond. This study tested the correlation between response to nCRT and intratumoral heterogeneity using next-generation sequencing assays. PATIENTS AND METHODS: DNA was extracted from formalin-fixed, paraffin-embedded biopsy samples from a cohort of patients with locally advanced rectal adenocarcinoma (T3/4 or N1/2 disease) who received nCRT. High read-depth sequencing of > 400 cancer-relevant genes was performed. Tumor mutations and variant allele frequencies were used to calculate mutant-allele tumor heterogeneity (MATH) scores as measures of intratumoral heterogeneity. Response to nCRT was pathologically scored after surgical resection. RESULTS: Biopsy samples from 21 patient tumors were analyzed. Eight patients had disease noted to have complete response, 2 moderate, 4 minimal, and 7 poor. Higher MATH scores correlated with poorer response to treatment, demonstrating significantly increased tumor heterogeneity compared to complete response (P = .039). CONCLUSION: The application of MATH scores as a measure of tumor heterogeneity may provide a useful biomarker for treatment response in locally advanced rectal cancer.