GemcitabineResistance Drug sensitivity Genomic profiles Breast cancer A B S T R A C TIncreasingly, the effectiveness of adjuvant chemotherapy agents for breast cancer has been related to changes in the genomic profile of tumors. We investigated correspondence between growth inhibitory concentrations of paclitaxel and gemcitabine (GI50) and gene copy number, mutation, and expression first in breast cancer cell lines and then in patients.Genes encoding direct targets of these drugs, metabolizing enzymes, transporters, and those previously associated with chemoresistance to paclitaxel (n ¼ 31 genes) or gemcitabine (n ¼ 18) were analyzed. A multi-factorial, principal component analysis (MFA) indicated expression was the strongest indicator of sensitivity for paclitaxel, and copy number and expression were informative for gemcitabine. The factors were combined using support vector machines (SVM). Expression of 15 genes (ABCC10, BCL2, BCL2L1, BIRC5, BMF, FGF2, FN1, MAP4, MAPT, NFKB2, SLCO1B3, TLR6, TMEM243, TWIST1, and CSAG2) predicted cell line sensitivity to paclitaxel with 82% accuracy. Copy number profiles of 3 genes (ABCC10, NT5C, TYMS ) together with expression of 7 genes (ABCB1, ABCC10, CMPK1, DCTD, NME1, RRM1, RRM2B), predicted gemcitabine response with 85% accuracy. Expression and copy number studies of two independent sets of patients with known responses were then analyzed with these models.These included tumor blocks from 21 patients that were treated with both paclitaxel and gemcitabine, and 319 patients on paclitaxel and anthracycline therapy. A new paclitaxel SVM was derived from an 11-gene subset since data for 4 of the original genes was * Corresponding author.E-mail address: progan@uwo.ca (P.K. Rogan).

Genomic aberrations and gene expression-defined subtypes in the large METABRIC patient cohort have been used to stratify and predict survival. The present study used normalized gene expression signatures of paclitaxel drug response to predict outcome for different survival times in METABRIC patients receiving hormone (HT) and, in some cases, chemotherapy (CT) agents. This machine learning method, which distinguishes sensitivity vs. resistance in breast cancer cell lines and validates predictions in patients; was also used to derive gene signatures of other HT  (tamoxifen) and CT agents (methotrexate, epirubicin, doxorubicin, and 5-fluorouracil) used in METABRIC. Paclitaxel gene signatures exhibited the best performance, however the other agents also predicted survival with acceptable accuracies. A support vector machine (SVM) model of paclitaxel response containing genes  ABCB1, ABCB11, ABCC1, ABCC10, BAD, BBC3, BCL2, BCL2L1, BMF, CYP2C8, CYP3A4, MAP2, MAP4, MAPT, NR1I2, SLCO1B3, TUBB1, TUBB4A, and TUBB4B was 78.6% accurate in predicting survival of 84 patients treated with both HT and CT (median survival ≥ 4.4 yr). Accuracy was lower (73.4%) in 304 untreated patients. The performance of other machine learning approaches was also evaluated at different survival thresholds. Minimum redundancy maximum relevance feature selection of a paclitaxel-based SVM classifier based on expression of genes  BCL2L1, BBC3, FGF2, FN1, and  TWIST1  was 81.1% accurate in 53 CT patients. In addition, a random forest (RF) classifier using a gene signature ( ABCB1, ABCB11, ABCC1, ABCC10, BAD, BBC3, BCL2, BCL2L1, BMF, CYP2C8, CYP3A4, MAP2, MAP4, MAPT, NR1I2,SLCO1B3, TUBB1, TUBB4A, and TUBB4B) predicted >3-year survival with 85.5% accuracy in 420 HT patients. A similar RF gene signature showed 82.7% accuracy in 504 patients treated with CT and/or HT. These results suggest that tumor gene expression signatures refined by machine learning techniques can be useful for predicting survival after drug therapies.

AimsThe objective of this study was to develop and validate an open-source digital pathology tool, QuPath, to automatically quantify CD138-positive bone marrow plasma cells (BMPCs).MethodsWe analysed CD138-scanned slides in QuPath. In the initial training phase, manual positive and negative cell counts were performed in representative areas of 10 bone marrow biopsies. Values from the manual counts were used to fine-tune parameters to detect BMPCs, using the positive cell detection and neural network (NN) classifier functions. In the testing phase, whole-slide images in an additional 40 cases were analysed. Output from the NN classifier was compared with two pathologist’s estimates of BMPC percentage.ResultsThe training set included manual counts ranging from 2403 to 17 287 cells per slide, with a median BMPC percentage of 13% (range: 3.1%–80.7%). In the testing phase, the quantification of plasma cells by image analysis correlated well with manual counting, particularly when restricted to BMPC percentages of <30% (Pearson’s r=0.96, p<0.001). Concordance between the NN classifier and the pathologist whole-slide estimates was similarly good, with an intraclass correlation of 0.83 and a weighted kappa for the NN classifier of 0.80 with the first rater and 0.90 with the second rater. This was similar to the weighted kappa between the two human raters (0.81).ConclusionsThis represents a validated digital pathology tool to assist in automatically and reliably counting BMPC percentage on CD138-stained slides with an acceptable error rate.

Genomic aberrations and gene expression-defined subtypes in the large METABRIC patient cohort have been used to stratify and predict survival. The present study used normalized gene expression signatures of paclitaxel drug response to predict outcome for different survival times in METABRIC patients receiving hormone (HT) and, in some cases, chemotherapy (CT) agents. This machine learning method, which distinguishes sensitivity vs. resistance in breast cancer cell lines and validates predictions in patients; was also used to derive gene signatures of other HT  (tamoxifen) and CT agents (methotrexate, epirubicin, doxorubicin, and 5-fluorouracil) used in METABRIC. Paclitaxel gene signatures exhibited the best performance, however the other agents also predicted survival with acceptable accuracies. A support vector machine (SVM) model of paclitaxel response containing genes  ABCB1, ABCB11, ABCC1, ABCC10, BAD, BBC3, BCL2, BCL2L1, BMF, CYP2C8, CYP3A4, MAP2, MAP4, MAPT, NR1I2, SLCO1B3, TUBB1, TUBB4A, and TUBB4B was 78.6% accurate in predicting survival of 84 patients treated with both HT and CT (median survival ≥ 4.4 yr). Accuracy was lower (73.4%) in 304 untreated patients. The performance of other machine learning approaches was also evaluated at different survival thresholds. Minimum redundancy maximum relevance feature selection of a paclitaxel-based SVM classifier based on expression of genes  BCL2L1, BBC3, FGF2, FN1, and  TWIST1  was 81.1% accurate in 53 CT patients. In addition, a random forest (RF) classifier using a gene signature ( ABCB1, ABCB11, ABCC1, ABCC10, BAD, BBC3, BCL2, BCL2L1, BMF, CYP2C8, CYP3A4, MAP2, MAP4, MAPT, NR1I2,SLCO1B3, TUBB1, TUBB4A, and TUBB4B) predicted >3-year survival with 85.5% accuracy in 420 HT patients. A similar RF gene signature showed 82.7% accuracy in 504 patients treated with CT and/or HT. These results suggest that tumor gene expression signatures refined by machine learning techniques can be useful for predicting survival after drug therapies.

Ependymomas are a heterogeneous group of central nervous system tumors. Despite the recent advances, there are no specific biomarkers for ependymomas. In this study, we explored the role of homeobox (HOX) genes and long noncoding RNA (LncRNA) HOTAIR in ependymomas along the neural axis. Bioinformatics analysis was performed on publicly available gene expression data. Quantitative RT-PCR was used to determine the mRNA expression level among different groups of ependymomas. RNA in situ hybridization (ISH) with probes specific to HOTAIR was performed on tumor tissue microarray (TMA) constructed with 19 ependymomas formalin-fixed paraffin-embedded tissue. Gene expression analysis revealed higher expression of posterior HOX genes and HOTAIR in myxopapillary ependymoma (MPE), in comparison to other spinal and intracranial ependymoma. qRT-PCR confirmed the high HOXD10 expression in spinal MPEs. There was a significant upregulation of HOTAIR expression in spinal MPE and elevated HOTAIR expressions were further confirmed by RNA ISH on the TMA. Intriguingly, HOXD10 and HOTAIR expressions were not elevated in nonependymoma spinal tumors. Our collective results suggest an important role for the lncRNA HOTAIR and posterior HOX genes in the tumorigenesis of spinal MPE. HOTAIR may also serve as a potential diagnostic marker for spinal MPE.

Granular cell tumors (GCT) are rare soft tissue neoplasms, which seldom occur in the vulva. They are more commonly benign, but malignant GCT do occur. We report a case of a 50-yr-old postmenopausal woman who presented with a vulvar lesion that was diagnosed as GCT on biopsy. Imaging and clinical examination revealed an enlarged, likely positive lymph node. Pathology of the subsequently resected total deep vulvectomy specimen showed 2 histologically distinct GCTs. The larger lesion met criteria for malignancy and histologically corresponded to metastatic deposits seen in the pelvic lymph nodes. The separate smaller lesion was histologically benign. This case illustrates a malignant GCT with a synchronous, likely benign GCT both occurring in the vulva. Our case demonstrates the application of histologic criteria in the diagnosis of malignant and benign GCT with discussion on the diagnosis and treatment of this rare tumor.

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Purpose The authors previously found that attending physicians conceptualize hospital admission purpose according to 3 perspectives: one focused dominantly on discharge, one on monitoring and managing chronic conditions, and one on optimizing overall patient health. Given implications of varying perspectives for patient care and team collaboration, this study explored how purpose of admission is negotiated and enacted within clinical teaching teams. Method Direct observations and field interviews took place in 2 internal medicine teaching units at 2 teaching hospitals in Ontario, Canada, in summer 2017. A constructivist grounded theory approach was used to inform data collection and analysis. Results The 54 participants included attendings, residents, and medical students. Management decisions were identified across 185 patients. Attendings and senior medical residents (second- and third-year residents) were each observed to enact one dominant perspective, while junior trainees (first-year residents and students) appeared less fixed in their perspectives. Teams were not observed discussing purpose of admission explicitly; however, differing perspectives were present and enacted. These differences became most noticeable when at the extremes (discharge focused vs optimization focused) or between senior medical residents and attendings. Attendings implicitly signaled and enforced their perspectives, using authority to shut down and redirect discussion. Trainees’ maneuvers for enacting their perspectives ranged from direct advocacy to covert manipulation (passive avoidance/forgetting and delaying until attending changeover). Conclusions Failing to negotiate and explicitly label perspectives on purpose of admission may lead to attendings and senior medical residents working at cross-purposes and to trainees participating in covert maneuvers, potentially affecting trust and professional identify development.