Comparative evaluation of functional outcome and pain relief after pulsed radiofrequency of the saphenous nerve within and distal to the adductor canal in medial compartment knee osteoarthritis: A randomized double-blind trial

Background and Aims

Pulsed radiofrequency (PRF) of the saphenous nerve (SN) has shown effective pain relief in knee pain because of knee osteoarthritis (KOA). The adductor canal (AC) contains other sensory nerves innervating the medial part of the knee joint apart from SN. We compared the PRF of SN within and outside the AC for their quality and duration of pain relief in knee osteoarthritis of the medial compartment (KOA-MC).

Material and Methods

We conducted a randomized prospective study in 60 patients with anteromedial knee pain because of KOA-MC. Patients in group A received PRF-SN, and those in group B received PRF-AC. The primary objectives were comparison of pain by Visual Analog Scale (VAS) scores and changes in quality of daily living by Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and OXFORD knee scores. The secondary objectives were comparison of analgesic requirements using Medicine Quantification Scale (MQS) scores and block-related complications. Intra-group comparison was performed by analysis of variance. Inter-group normally distributed data were assessed by Student's t-test, non-normally distributed and ordinal data were assessed by Mann-Whitney U-test, and categorical data were assessed by Chi-square test. A P value of <0.05 was considered significant.

Results

VAS scores were significantly lower in Gr-B at 12 weeks. The WOMAC scores and OXFORD scores at 4, 8, 12, and 24 weeks were significantly lower in Gr-B compared to Gr-A.

Conclusion

The PRF-AC provides better pain relief and functional outcome than PRF-SN; however, duration of pain relief was not significantly different.

Functional interrogation of twenty type 2 diabetes-associated genes using isogenic human embryonic stem cell-derived β-like cells

Genetic studies have identified numerous loci associated with type 2 diabetes (T2D), but the functional roles of many loci remain unexplored. Here, we engineered isogenic knockout human embryonic stem cell lines for 20 genes associated with T2D risk. We examined the impacts of each knockout on β cell differentiation, functions, and survival. We generated gene expression and chromatin accessibility profiles on β cells derived from each knockout line. Analyses of T2D-association signals overlapping HNF4A-dependent ATAC peaks identified a likely causal variant at the FAIM2 T2D-association signal. Additionally, the integrative association analyses identified four genes (CP, RNASE1, PCSK1N, and GSTA2) associated with insulin production, and two genes (TAGLN3 and DHRS2) associated with β cell sensitivity to lipotoxicity. Finally, we leveraged deep ATAC-seq read coverage to assess allele-specific imbalance at variants heterozygous in the parental line and identified a single likely functional variant at each of 23 T2D-association signals.

Prediction of cancer treatment response from histopathology images through imputed transcriptomics

Advances in artificial intelligence have paved the way for leveraging hematoxylin and eosin (H&E)-stained tumor slides for precision oncology. We present ENLIGHT-DeepPT, an approach for predicting response to multiple targeted and immunotherapies from H&E-slides. In difference from existing approaches that aim to predict treatment response directly from the slides, ENLIGHT-DeepPT is an indirect two-step approach consisting of (1) DeepPT, a new deep-learning framework that predicts genome-wide tumor mRNA expression from slides, and (2) ENLIGHT, which predicts response based on the DeepPT inferred expression values. DeepPT successfully predicts transcriptomics in all 16 TCGA cohorts tested and generalizes well to two independent datasets. Our key contribution is showing that ENLIGHT-DeepPT successfully predicts true responders in five independent patients' cohorts involving four different treatments spanning six cancer types with an overall odds ratio of 2.44, increasing the baseline response rate by 43.47% among predicted responders, without the need for any treatment data for training. Furthermore, its prediction accuracy on these datasets is comparable to a supervised approach predicting the response directly from the images, which needs to be trained and tested on the same cohort. ENLIGHT-DeepPT future application could provide clinicians with rapid treatment recommendations to an array of different therapies and importantly, may contribute to advancing precision oncology in developing countries.

CAM Based Fine-grained Spatial Feature Supervision On Surgical-PPE: A New Dataset For Surgical PPE Kit Presence Detection

In the wake of Covid pandemic, usage of surgical PPE kit by surgeons has become essential. Since reliable localization of human joints is necessary for automated understanding of surgeons activity, the first step is surgical PPE kit detection. While there exist reported works on industrial PPE kit detection, task of surgical PPE kit detection has hardly been explored. To facilitate this, we construct "Surgical-PPE" dataset with 1150 Non-PPE instances and 2656 surgeon wearing PPE kit instances. In this work, we also propose a two-stage transfer learning based end-to-end training methodology. Novelty lies in (a) novel "Surgical-PPE" dataset to detect if surgeon is wearing PPE kit or not, (b) proposed supervised contrastive combined loss function for stage-1 training, (c) proposed spatial context aware combined loss function for stage-2 training. We qualitatively illustrate the improvement of HiResCAM and XGrad-CAM explanations for the proposed methodology. We also qualitatively illustrate that feature embeddings of same class are pulled closer together compared to feature embeddings of different classes on the proposed multi-stage training methodology, using T-SNE plots. We benchmark the performance of popular existing network architectures along with the proposed methodology on "Surgical-PPE" dataset. Using proposed methodology, we achieve peak accuracy of 97.63%, precision of 97.66%, recall of 97.63%, F1-score of 97.64%, JI of 95.41% and FPR of 2.5%. We report improvement by 1.7% in terms of FPR and 2% in terms of JI compared to second best performing model (ResNext50(CE)). Owing to the proposed training methodology, an improvement of 2.62% in terms of FPR and 5% in terms of JI was observed.Clinical relevance- To understand the OT activity of the surgeon in third-person perspective, it is important to determine whether or not, the surgeon is wearing PPE kit. Hence surgical PPE kit presence detection becomes the first step towards automated surgical video analysis.

Functional interrogation of twenty type 2 diabetes-associated genes using isogenic hESC-derived β-like cells

Genetic studies have identified numerous loci associated with type 2 diabetes (T2D), but the functional role of many loci has remained unexplored. In this study, we engineered isogenic knockout human embryonic stem cell (hESC) lines for 20 genes associated with T2D risk. We systematically examined β-cell differentiation, insulin production and secretion, and survival. We performed RNA-seq and ATAC-seq on hESC-β cells from each knockout line. Analyses of T2D GWAS signals overlapping with HNF4A-dependent ATAC peaks identified a specific SNP as a likely causal variant. In addition, we performed integrative association analyses and identified four genes ( CP, RNASE1, PCSK1N and GSTA2 ) associated with insulin production, and two genes ( TAGLN3 and DHRS2 ) associated with sensitivity to lipotoxicity. Finally, we leveraged deep ATAC-seq read coverage to assess allele-specific imbalance at variants heterozygous in the parental hESC line, to identify a single likely functional variant at each of 23 T2D GWAS signals.

Abstract 4355: Prediction of cancer treatment response from histopathology images for a broad set of treatments and indications

Background: Advances in artificial intelligence have paved the way for predicting cancer patients’ survival and response to treatment from hematoxylin and eosin (H&E)-stained tumor slides. Extant approaches do so either via prediction of actionable mutations and gene fusions, or directly from the H&E images by training a task-specific model using large treatment outcome data.

Methods: Here we present the first approach for predicting patient response to multiple targeted treatments and immunotherapies directly from H&E slides. It is founded on two conceptual steps: (1) First, we developed DeepPT, a deep-learning framework that trains on formalin-fixed, paraffin-embedded (FFPE) TCGA whole slide images and their corresponding gene expression profiles to predict wide-scale tumor gene expression from the slides. (2) Second, we apply ENLIGHT, a published approach that predicts individual responses to a wide range of targeted and immunotherapies based on the tumor biopsy measured transcriptomics. Here we apply ENLIGHT to predict patient treatment response from the DeepPT predicted expression values instead of those measured directly from the tumor, notably, without any further training or adaptation of ENLIGHT.

Results: First, we find that DeepPT generalizes well to predicting gene expression in all 13 TCGA cohorts tested in cross-validation and importantly, in two independent unseen breast and brain cancer datasets. DeepPT outperforms HE2RNA, a state-of-the-art algorithm for the same task. Second, we demonstrate that the combined DeepPT/ENLIGHT pipeline (termed ENLIGHT-DeepPT) successfully predicts true responders using the original ENLIGHT decision threshold with odds ratios of 1.5 - 4.5, increasing the baseline response rates by 15-85% among predicted responders in five independent unseen cohorts of diverse cancer types and treatments. Remarkably, in one dataset where matched data was available, ENLIGHT-DeepPT has similar performance to that obtained by a supervised learning algorithm that was recently published in Nature, trained on the same cohort.

Conclusions: We present for the first time a general framework for predicting patient response to a broad array of targeted and checkpoint therapies from histopathological images, without reliance on treatment outcome data, which are yet scarce and challenging to obtain. Importantly, ENLIGHT-DeepPT offers clinicians real-time treatment recommendations when one cannot wait for sequencing results. Also, and due to its very low cost, we very much hope that it will facilitate the advent of precision oncology in developing countries.

Citation Format: Danh-Tai Hoang, Gal Dinstag, Leandro C. Hermida, Doreen S. Ben-Zvi, Efrat Elis, Katherine Caley, Sanju Sinha, Neelam Sinha, Christopher H. Dampier, Tuvik Beker, Kenneth Aldape, Ranit Aharonov, Eric A. Stone, Eytan Ruppin. Prediction of cancer treatment response from histopathology images for a broad set of treatments and indications. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4355.

Abstract 3115: Predicting in-depth mechanism of action of cancer drugs

Knowing a drug’s mechanism of action (MOA) is essential for its clinical success by selecting the best indications, likely responders, and combinations. Yet knowledge of many drugs’ MOA remains lacking. Here we present DeepTarget, a computational tool for deep characterization of cancer drugs’ MOA by integrating existing large-scale genetic and drug screens. Spanning ∼1500 drugs across ∼18K possible target genes, DeepTarget predicts: (1) a drug’s primary target(s), (2) whether it specifically targets the wild-type or mutated target forms, and (3) the secondary target(s) that mediate its response when the primary target is not expressed. We first tested and successfully validated DeepTarget in a total of eleven unseen gold-standard datasets, with an average AUC of 0.82, 0.77, and 0.92 for the above three prediction abilities, respectively. We then proceed to use it in a wide range of applications: First, we find that DeepTarget’s predicted specificity of a drug to its target is strongly associated with its success in clinical trials and is higher in its FDA-approved indications. Second, DeepTarget predicts candidate drugs for targeting currently undruggable cancer oncogenes and their mutant forms. Finally, DeepTarget predicts new targets for drugs with unknown MOA and new secondary targets of approved drugs. Taken together, DeepTarget is a new computational framework for accelerating drug prioritization and its target discovery by leveraging large-scale genetic and drug screens.

Citation Format: Sanju Sinha, Neelam Sinha, Eytan Ruppin. Predicting in-depth mechanism of action of cancer drugs [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3115.

The Complex-valued PD-net for MRI reconstruction of knee images

MRI reconstruction is the fundamental task of obtaining diagnostic quality images from MRI sensor data and is an active area of research for improving accuracy, speed and memory requirements of the process. Complex-valued neural networks have previously achieved superior MRI reconstructions compared to real-valued nets. But those works operated in the image domain to denoise poor quality reconstructions of the raw sensor (k-space) data. Also small-scale or proprietary datasets with few clinical images or raw k-space volumes were used in these works, and none of the works use publicly available large-scale raw k-space datasets. Recent studies have shown that cross-domain neural networks for MRI reconstruction, or networks which leverage information from both k-space and image domains, have better potential than single-domain networks which operate only in one domain. We study the effects of complex-valued operations on a top-performing cross-domain neural network for MRI reconstruction called the Primal-Dual net, or PD-net. The PD-net is a fully convolutional architecture that takes input as raw k-space data and outputs the reconstructions, thus performing both the inversion and denoising tasks. We experiment with the publicly available, large-scale fastMRI single-coil knee dataset having 973 train volumes and 199 validation volumes. Our proposed method (Complex PD-net) achieves PSNR and SSIM of 33.3 dB and 0.8033 respectively, compared to 32.13 dB and 0.728 obtained by PD-net. Our Complex PD-net achieves 10.3% higher SSIM with just over 50% of the total parameters w.r.t. the SOTA methodology.

Predicting patient response to cancer therapy via histopathology images

e13561

Background: Histopathology has long been considered the gold standard of clinical diagnosis and prognosis in cancer. In recent years, molecular markers including tumor gene expression have proven increasingly valuable for enhancing diagnosis and precision oncology. Here we ask if we can predict tumor gene expression from its histopathology images and based on the latter, predict patient survival and treatment response. Methods: We developed DeepPT (Deep Pathology for Treatment), a deep learning framework that predicts gene expression directly from histopathology tumor images. DeepPT is composed of three main components: a pre-trained convolutional neural network model for feature extraction, an auto-encoder for feature compression, and a multiple-layer perceptron for regression. This architecture enables the model to capitalize on the similarity among the gene expressions and benefit from the advantages of multitask learning. Results: DeepPT was trained with haematoxylin and eosin stained (H&E) tumor slides from lung and breast cancer patients and their corresponding gene expression profiles. The models were then used to predict gene expression from five different held-out datasets, using nested cross validation. A total of approximately 23,000 genes were considered in this study; out of these, over 99% had a positive correlation between predicted and actual values, commonly for lung and breast cancer. Furthermore, a record number of genes (2,541 and 1,197 genes for lung and breast cancer, respectively) had a correlation above 0.4, well over the results of the current state-of-the-art approach (1,550 and 786 genes, respectively). We next studied if the inferred gene expression could be used for H&E-based personalized medicine. To this end, we used the predicted tumor transcriptomics generated by DeepPT as input to ENLIGHT, a platform that predicts a patient’s response to treatment from their tumor transcriptomics. We found that ENLIGHT matching scores based on DeepPT outputs were indeed associated with response to treatment. Conclusions: DeepPT is the first computational approach for building response predictors that can infer therapy response directly from whole slide images of patient biopsies. Importantly, its future application promises to make precision oncology more accessible to physicians and patients in the developing world.

Comparison of the analgesic efficacy of ultrasound-guided transmuscular quadratus lumborum block versus thoracic erector spinae block for postoperative analgesia in caesarean section parturients under spinal anaesthesia-A randomised study

Background and Aims

Truncal blocks play an important role in multimodal analgesia regimens to manage the postoperative pain after lower segment caesarean section (LSCS). This study was aimed to compare the analgesic efficacy of ultrasound (US)-guided transmuscular quadratus lumborum block (TQLB) and thoracic erector spinae plane block (TESPB) in parturients of LSCS done under subarachnoid block (SAB).

Methods

In a randomised and double blind study, 60 parturients scheduled for LSCS under spinal anaesthesia were randomly divided into two equal groups: group E (n = 30) and group Q (n = 30). After surgery, each parturient received either US guided bilateral TQLB (group Q) or TESPB (group E) with 20 ml 0.375% ropivacaine and 4 mg dexamethasone on each side. Assessments were done at 2, 4, 6, 8, 10, 12 and 24 h. The primary objective was to compare the duration of analgesia (first request to rescue analgesia) and the secondary objectives were to compare pain scores [numerical rating score (NRS)], total amount of tramadol consumption, incidence of nausea-vomiting, parturient satisfaction and other adverse effects in 24 hours postoperatively.

Results

The duration of analgesia (mean ± standard deviation) was comparable in group E (11.90 ± 2.49 h) and group Q (12.56 ± 3.38 h), P = 0.19. Pain scores (NRS) at rest and on movement were comparable at all time points of 2, 4, 6, 8, 10, 12, and 24 h (P > 0.05). The amount of tramadol used was comparable in group E and group Q (P = 0.48).

Conclusion

TESPB and TQLB are equally efficacious to provide postoperative analgesia after LSCS done under SAB when used as a part of multimodal analgesia.

Immune determinants of the association between tumor mutational burden and immunotherapy response across cancer types

The FDA has recently approved a high tumor mutational burden (TMB-high) biomarker, defined by {greater than or equal to}10 mutations/Mb, for the treatment of solid tumors with pembrolizumab, an immune checkpoint inhibitor (ICI) that targets PD1. However, recent studies have shown that this TMB-high biomarker is only able to stratify ICI responders in a subset of cancer types, and the mechanisms underlying this observation have remained unknown. The tumor immune microenvironment (TME) may modulate the stratification power of TMB (termed TMB power), determining if it will be predictive of ICI response in a given cancer type. To systematically study this hypothesis, we inferred the levels of 31 immune-related factors characteristic of the TME of different cancer types in The Cancer Genome Atlas (TCGA). Integration of this information with TMB and response data of 2,277 patients treated with anti-PD1 identified key immune factors that determine TMB power across 14 different cancer types. We find that high levels of M1 macrophages and low resting dendritic cells in the TME characterized cancer types with high TMB power. A model based on these two immune factors strongly predicted TMB power in a given cancer type during cross-validation and testing (Spearman Rho=0.76 & 1, respectively). Using this model, we predicted the TMB power in nine additional cancer types, including rare cancers, for which TMB and ICI response data are not yet publicly available. Our analysis indicates that TMB-high may be highly predictive of ICI response in cervical squamous cell carcinoma, suggesting that such a study should be prioritized.

Landmark-guided pericapsular nerve group (PENG) block for reduction of dislocated prosthetic hip: A case report

Dislocated hip joint is a painful condition, which requires urgent reduction. Previously, ultrasound (US)-guided pericapsular nerve group (PENG) block has been used for reduction of dislocated prosthetic hip. We have used landmark-guided PENG block in two patients of dislocation of prosthetic hip. We suggest that the landmark-guided technique of PENG block can be used safely and successfully as an alternative technique, where US facility is not available.

Abstract P054: Immune determinants of the association between tumor mutational burden and immunotherapy response across cancer types

The FDA has recently approved a high tumor mutational burden (TMB-High, defined by ≥10 mutations/Mb) as a biomarker for the treatment of advanced solid tumors with pembrolizumab, an immune checkpoint inhibitor (ICI) that targets PD1. However, recent studies have shown that this TMB-high biomarker is only able to stratify ICI responders in a subset of cancer types, where the mechanisms underlying this observation have remained unknown. We hypothesized that the tumor immune microenvironment (TME) may determine the ability of high-TMB to stratify responders of ICI (termed TMB power) in each cancer type. To systematically study this hypothesis, we first inferred the levels of 31 immune-related factors characteristic of the TME of different cancer types in the TCGA. We next integrated this information with a cohort of 2,277 ICI-treated patients with TMB and response measures, to identify the key immune factors that can determine TMB power across 14 different cancer types. This cohort was created by collating the largest publicly available cohort comprising 1959 patients together with a new cohort of 318 patients, where TMB has been quantified using the MSK-IMPACT panel. We find that high levels of M1 macrophages and low levels of resting dendritic cells in the TME characterize cancer types with high TMB power. These findings are aligned with prior reports that M1 macrophages could be anti-tumorigenic by fostering an inflammation response and activating CD8 T cells against tumor, and that resting dendritic cells may induce tolerance to tumor antigens via inducing T cell death or their anergic state. A model based on these two immune factors strongly predicts TMB power across cancer types (Spearman Rho=0.76, P&lt;3.6E-04). Using this model, we provide predictions of the TMB power in nine additional cancer types, including rare cancers, for which TMB and ICI response data are not yet publicly available on a large scale. These predictions can be used to prioritize the clinical trials testing the usage of TMB-high biomarker in new cancer types. In this line, our analysis indicates that TMB-High may be strongly predictive of ICI response in cervical squamous cell carcinoma, suggesting that such a study should be prioritized.

Citation Format: Neelam Sinha, Sanju Sinha, Christina Valero, Alejandro A. Schaffer, Kenneth Aldape, Kevin Litchfield, Timothy A. Chan, Luc G. T. Morris, Eytan Ruppin. Immune determinants of the association between tumor mutational burden and immunotherapy response across cancer types [abstract]. In: Abstracts: AACR Virtual Special Conference: Tumor Immunology and Immunotherapy; 2021 Oct 5-6. Philadelphia (PA): AACR; Cancer Immunol Res 2022;10(1 Suppl):Abstract nr P054.

Determining chromosomal arms 1p/19q co-deletion status in low graded glioma by cross correlation-periodogram pattern analysis

Prediction of mutational status of different graded glioma is extremely crucial for its diagnosis and treatment planning. Currently FISH and the surgical biopsy techniques are the ‘gold standard’ in the field of diagnostics; the analyses of which helps to decide appropriate treatment regime. In this study we proposed a novel approach to analyze structural MRI image signature pattern for predicting 1p/19q co-deletion status non-invasively. A total of 159 patients with grade-II and grade-III glioma were included in the analysis. These patients earlier underwent biopsy; the report of which confirmed 57 cases with no 1p/19q co-deletion and 102 cases with 1p/19q co-deletion. Tumor tissue heterogeneity was investigated by variance of cross correlation (VoCC). Significant differences in the pattern of VoCC between two classes was quantified using Lomb-Scargle (LS) periodogram. Energy and the cut-off frequency of LS power spectral density were derived and utilized as the features for classification. RUSBoost classifier was used that yield highest classification accuracy of 84% for G-II and 87% for G-III glioma respectively in classifying 1p/19q co-deleted and 1p/19q non-deleted glioma. In clinical practice the proposed technique can be utilized as a non-invasive pre-confirmatory test of glioma mutation, before wet-lab validation.

Spatio-Temporal Features Based Surgical Phase Classification Using CNNs

In this paper, we propose a novel encoder-decoder based surgical phase classification technique leveraging on the spatio-temporal features extracted from the videos of laparoscopic cholecystectomy surgery. We use combined margin loss function to train on the computationally efficient PeleeNet architecture to extract features that exhibit: (1) Intra-phase similarity, (2) Inter-phase dissimilarity. Using these features, we propose to encapsulate sequential feature embeddings, 64 at a time and classify the surgical phase based on customized efficient residual factorized CNN architecture (ST-ERFNet). We obtained surgical phase classification accuracy of 86.07% on the publicly available Cholec80 dataset which consists of 7 surgical phases. The number of parameters required for the computation is approximately reduced by 84% and yet achieves comparable performance as the state of the art.Clinical relevance- Autonomous surgical phase classification sets the platform for automatically analyzing the entire surgical work flow. Additionally, could streamline the process of assessment of a surgery in terms of efficiency, early detection of errors or deviation from usual practice. This would potentially result in increased patient care.

Whole Tumor Segmentation from Brain MR images using Multi-view 2D Convolutional Neural Network

In this paper, a study is reported on the popular BraTS dataset for segmentation of brain tumor. The BraTS 2019 dataset is used that comprises four MR modalities along with the ground-truth for 259 high grade glioma (HGG) and 76 low grade glioma (LGG) patient data. We have employed U-Net architecture based 2D convolutional neural network (CNN) for each of the orthogonal planes (sagittal, coronal and axial) and fused their predictions. The objective function is aimed to minimize Dice loss between the binary prediction and its actual labels. Samples having tumor information are considered for each patient data to avoid training on non-informative data. The models are trained on 222 HGG data and tested on 37 HGG data using performance metrics such as sensitivity, specificity, accuracy and Dice score. Test-time augmentation is also performed to improve the segmentation performance. 7-fold cross validation is conducted to analyze the performance on different sets of training and testing data.

Comparison of supra-inguinal fascia iliaca versus pericapsular nerve block for ease of positioning during spinal anaesthesia: A randomised double-blinded trial

Background and Aims

Regional analgesic techniques such as supra-inguinal fascia-iliaca compartment block (S-FICB) and pericapsular nerve group (PENG) block have been found to be effective in providing good pain relief in hip-fracture patients. However, comparative studies between PENG and S-FICB are lacking. The aim of this study was to compare the analgesic efficacy of S-FICB and PENG block and assess their efficacy in optimal patient positioning for spinal anaesthesia.

Methods

A prospective randomised double-blind study was conducted in 66 patients randomly divided to receive either S-FICB or PENG block under ultrasound guidance. Primary outcome measures were numerical rating scale (NRS) pain score at rest and on passive 15° limb lifting, 30 minutes after the block and ease of spinal positioning. The secondary outcome measures were NRS over 24 hours, amount of tramadol used (number of rescue doses), patients' satisfaction and block-related complications. The results were analysed using statistical software (MedCalc version 19.2.1). Continuous and categorical data were analysed using appropriate statistical analysis and P < 0.05 was considered significant.

Results

Post-block, the NRS score decreased significantly in PENG and S-FICB groups at rest and movement (P < 0.0001). The EOSP score was significantly better in PENG group (P < 0.0001). First analgesic request and pain relief in the first 24-hour period were similar between the groups (P = 0.524).

Conclusion

PENG block provided better pain relief and ease of positing during SA in patients with fractured hip scheduled for hip surgery.

A comprehensive map of alternative polyadenylation in African American and European American lung cancer patients

Deciphering the post-transcriptional mechanisms (PTM) regulating gene expression is critical to understand the dynamics underlying transcriptomic regulation in cancer. Alternative polyadenylation (APA)-regulation of mRNA 3'UTR length by alternating poly(A) site usage-is a key PTM mechanism whose comprehensive analysis in cancer remains an important open challenge. Here we use a method and analysis pipeline that sequences 3'end-enriched RNA directly to overcome the saturation limitation of traditional 5'-3' based sequencing. We comprehensively map the APA landscape in lung cancer in a cohort of 98 tumor/non-involved tissues derived from European American and African American patients. We identify a global shortening of 3'UTR transcripts in lung cancer, with notable functional implications on the expression of both coding and noncoding genes. We find that APA of non-coding RNA transcripts (long non-coding RNAs and microRNAs) is a recurrent event in lung cancer and discover that the selection of alternative polyA sites is a form of non-coding RNA expression control. Our results indicate that mRNA transcripts from EAs are two times more likely than AAs to undergo APA in lung cancer. Taken together, our findings comprehensively map and identify the important functional role of alternative polyadenylation in determining transcriptomic heterogeneity in lung cancer.

Quadratus lumborum or transversus abdominis plane block for postoperative analgesia after cesarean: a double-blinded randomized trial

Background

Multimodal analgesia (MMA) is the current standard practice to provide post-cesarean analgesia. The aim of this study was to compare the analgesic efficacy of quadratus lumborum (QL) block and transversus abdominis plane (TAP) block as an adjunct to MMA.

Methods

Eighty mothers undergoing cesarean delivery under spinal anesthesia were randomized to receive either TAP or transmuscular QL block (QLB) with 20 mL 0.375% ropivacaine on each side. Postoperatively, all the subjects were assessed at 2, 4, 6, 8, 12, 18, and 24 hours. The primary outcome was the time to first analgesic request. The secondary outcomes were the pain scores during rest and movement, number of doses of tramadol, postoperative nausea-vomiting, sedation, and mother’s satisfaction with the pain management.

Results

The median (IQR) time to first analgesic request was 12 (9.25, 13) hours in the QL group and 9 (8.25, 11.37) hours in the TAP group (p = 0.0008). Patients in QL group consumed less doses of tramadol than those in TAP group (p < 0.0001). Pain scores were significantly lower in the QL group at all time points (p < 0.0001) except at 8^th hour when at rest, p = 0.0024, and on movement, p = 0.0028. The maternal satisfaction was significantly higher in the QL group (p = 0.0017).

Conclusion

Our study showed the significant delay in time to first analgesic request in QL group patients. Patients in the QL group had lower pain scores, required fewer analgesic supplements, and had more satisfaction. Nausea-vomiting and sedation were comparable.

Abstract 29: The recently approved high-TMB criteria may introduce a sex bias in response to PD1 inhibitors

Motivation and research question: The U.S. Food and Drug Administration recently approved treatment with pembrolizumab, an immune checkpoint inhibitor (ICI) targeting PD1 (anti-PD1), for all advanced solid tumors with high tumor mutational burden (TMB), defined as ≥10 mutations/Megabase (mut/Mb). Recent studies have suggested that strength of immune selection, biomarkers of outcome, TMB levels and response to ICI treatment may differ between male and female cancer patients in some tumor types. This motivated us to examine what are the sex-specific implications, if any, of the ≥10 mut/Mb threshold on selecting patients for ICI treatment.

Data & Methods: We analyzed the largest ICI cohort publicly available to date (Samstein et al. 2019), including 1,070 patients treated with anti-PD1/PD-L1 monotherapy, 99 treated with anti-CTLA4 and 255 treated with an ICI combination. We focused on the nine solid tumor types with TMB and clinical response data (median follow-up of 19 months) for at least 50 patients.

Results: 1.We observed a significant difference in the median TMB across sex among melanoma patients (n=130, female vs male median TMB=8.36 vs 11.81, P&lt;0.02), in concordance with previous observations.2.Notably, we found that the FDA-approved threshold of ≥10 mut/Mb to select patients would result in an unwarranted sex bias in melanoma patients. This threshold successfully identifies female melanoma patients with better overall survival (hazard ratio (HR) =0.19, P&lt;0.03) but fails to do so in male patients (HR=0.94, P&lt;0.85), resulting in a five times higher HR in males vs females at that threshold level (interaction between sex and TMB P&lt;0.03). This bias in effect size by sex is confirmed in two additional melanoma cohorts treated with anti-PD1 (Hugo et al. 2016, N=38, P&lt;0.024; Liu et al. 2016, N=114, P&lt;0.03; and P&lt;0.01 for all three melanoma datasets combined, N=312). We do not observe this differential effect for anti-CTLA4 treatment (P&lt;0.14, N=174) or for anti-PD1 + anti-CTLA4 combination (P&lt;0.8, N=115) or for non-ICI treatments (P&lt;0.4, N=322).3.Analysis of additional eight cancer types whose data are available in (Samstein et al 2019) points to glioblastoma (female vs male HR=0.87 vs 0.5) and cancer of unknown origin (female vs male HR= 1.03 vs 0.15), which show marked differences in the HR between male and female patients when applying the FDA threshold for TMB. However, despite these large effects, they do not achieve statistical significance, probably due to the small size of these datasets.

Conclusions:

The FDA-approved criteria of 10 mutations/Mb could serve as an informative biomarker for stratifying female melanoma patients but is inadequate for stratifying male patients for anti-PD1 treatment. Our results indicate that its usage is likely to introduce a sex bias in additional cancer types, which will be highly important to carefully test further in larger datasets.

Citation Format: Neelam Sinha, Sanju Sinha, Kuoyuan Cheng, Sanna Madan, Alejandro Schaffer, Kenneth Aldape, Ayelet Erez, Brid M. Ryan, Eytan Ruppin. The recently approved high-TMB criteria may introduce a sex bias in response to PD1 inhibitors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 29.

Efficacy of transmuscular quadratus lumborum block in the multimodal regimen for postoperative analgesia after total laparoscopic hysterectomy: A prospective randomised double-blinded study

Background and Aims

Transmuscular Quadratus Lumborum Block (TQLB) is a novel regional anaesthesia technique, however, its analgesic efficacy as a component of multimodal analgesia (MMA) in Total Laparoscopic Hysterectomy (TLH) is not well studied. The aim of the study was to evaluate the analgesic efficacy of TQLB as a component of MMA for postoperative pain in TLH.

Methods

A prospective double-blind randomised controlled study was done after approval from the ethical committee and informed patient consent. After randomisation, 37 patients in Group-Q received 20 ml 0.375% ropivacaine and in Group-C, 37 patients received saline in TQLB bilaterally after TLH surgery. All patients received intravenous patient controlled analgesia (IV-PCA) with fentanyl along with diclofenac 75 mg every 12 h. All the patients were assessed at 2, 4, 6, 8, 12, 18, and 24 hours. The primary outcome was the time to first analgesic request. The secondary outcome measures were total fentanyl consumption in 24 hrs, pain scores during rest and movement, postoperative nausea-vomiting, sedation and complications related to local anaesthetic and TQLB procedure.

Results

The mean [standard deviation (SD)] time to first analgesic request was 7.8 (1.5) hours in Group-Q and 3.2 (1.0) hours in Group-C (P < 0.0001). The mean (SD) dose of fentanyl used in 24 hours was 167.3 (44) μg in Group-Q and 226.5 (41.9) μg in Group-C (P < 0.0001).

Conclusion

The ultrasound-guided TQLB provides effective postoperative analgesia after TLH surgery in a multimodal analgesia approach. It reduces the fentanyl consumption and improves the visual analogue scale (VAS) score.

Consensus and development of document for management of stabilized acute decompensated heart failure with reduced ejection fraction in India

Aim

Ensuring adherence to guideline-directed medical therapy (GDMT) is an effective strategy to reduce mortality and readmission rates for heart failure (HF). Use of a checklist is one of the best tools to ensure GDMT. The aim was to develop a consensus document with a robust checklist for stabilized acute decompensated HF patients with reduced ejection fraction. While there are multiple checklists available, an India-specific checklist that is easy to fill and validated by regional and national subject matter experts (SMEs) is required.

Methodology

A total of 25 Cardiology SMEs who consented to participate from India discussed data from literature, current evidence, international guidelines and practical experiences in two national and four regional meetings.

Results

Recommendations included HF management, treatment optimization, and patient education. The checklist should be filled at four time points- (a) transition from intensive care unit to ward, (b) at discharge, (c) 1st follow-up and (d) subsequent follow-up. The checklist is the responsibility of the consultant or the treating physician which can be delegated to a junior resident or a trained HF nurse.

Conclusion

This checklist will ensure GDMT, simplify transition of care and can be used by all doctors across India. Institutions, associations, and societies should recommend this checklist for adaptability in public and private hospital. Hospital administrations should roll out policy for adoption of checklist by ensuring patient files have the checklist at the time of discharge and encourage practice of filling it diligently during follow-up visits.

Pericapsular nerve group (PENG) block: A feasibility study of landmark based technique

Background:

Pericapsular nerve group (PENG) block is a new ultrasound guided nerve block. It was used primarily to relieve pain in hip fracture; now, many new indications have been added. However, dependency on ultrasound guidance for this block limits its use where ultrasound facility is poor or not available. We have suggested a landmark based technique to increase the benefit of this novel nerve block.

Aim and Objectives:

To do a feasibility study to assess the successful placement of block needle, clinical efficacy of the block and block-related complications.

Material and Methods:

Total 10 patients (4 males and 6 females) with fracture hip and scheduled for hip surgery under spinal anaesthesia were selected for the study. In 4 patients ultrasound guided PENG block using out-of-plane approach and in 6 patients landmark based nerve stimulator guided block was given with 20ml 0.25% bupivacaine and 8mg dexamethasone. Pain relief before and after 30 minutes of block was assessed by numeric rating scale (NRS) and comfort during spinal position was assessed by ease of spinal position score (EOSP).

Results:

All 10 patients had successful block; NRS at rest was 6 (6-9) Vs 2 (0-2) and on 15 °limb elevation was 8 (8-10) Vs 3 (2-4). All patients could sit comfortably during spinal anaesthesia and median (range) EOSP sore was 3 (2-3). No complication was observed.

Conclusion:

Landmark based technique for PENG block is a feasible option and can be used safely where ultrasound facility is not available.

Higher prevalence of homologous recombination deficiency in tumors from African Americans versus European Americans

To improve our understanding of longstanding disparities in incidence and mortality in lung cancer across ancestry, we performed a systematic comparative analysis of molecular features in tumors from African Americans (AAs) and European Americans (EAs). We find that lung squamous cell carcinoma tumors from AAs exhibit higher genomic instability-the proportion of non-diploid genome-aggressive molecular features such as chromothripsis and higher homologous recombination deficiency (HRD). In The Cancer Genome Atlas, we demonstrate that high genomic instability, HRD and chromothripsis among tumors from AAs is found across many cancer types. The prevalence of germline HRD (that is, the total number of pathogenic variants in homologous recombination genes) is higher in tumors from AAs, suggesting that the somatic differences observed have genetic ancestry origins. We also identify AA-specific copy-number-based arm-, focal- and gene-level recurrent features in lung cancer, including higher frequencies of PTEN deletion and KRAS amplification. These results highlight the importance of including under-represented populations in genomics research.

Computational fluid dynamics analysis of the hydraulic (filtration) efficiency of a residential swimming pool

Hydraulic or filtration efficiency of residential swimming pools, quantified in terms of residence time characteristics, is critical to disinfection and thus important to public health. In this study, a three-dimensional computational fluid dynamics model together with Eulerian and Lagrangian-based techniques are used for investigating the residence time characteristics of a passive tracer and particles in the water, representative of chemicals and pathogens, respectively. The flow pattern in the pool is found to be characterized by dead zone regions where water constituents may be retained for extended periods of times, thereby potentially decreasing the pool hydraulic efficiency. Two return-jet configurations are studied in order to understand the effect of return-jet location and intensity on the hydraulic efficiency of the pool. A two-jet configuration is found to perform on par with a three-jet configuration in removing dissolved constituents but the former is more efficient than the latter in removing or flushing particles. The latter result suggests that return-jet location and associated flow circulation pattern have an important impact on hydraulic efficiency. Thus return-jet configuration should be incorporated as a key parameter in the design of swimming pools complementing current design standards.

Isotopic study of intraseasonal variations of plant transpiration: an alternative means to characterise the dry phases of monsoon

The isotopic characteristics of plant transpired water are strongly controlled by soil evaporation process, primarily by relative humidity. The monsoon system is characterised by large variability of several atmospheric parameters; the primary one being the rainfall, which in turn, modulates the relative humidity. Due to the strong dependency of transpiration on relative humidity, it is expected that this process would vary in accordance with the active and break periods of the monsoon season, which are known to produce cycles of humid and relatively dry phases during a monsoon season. To study the transpiration process, an experiment was conducted wherein rainwater and transpired water were collected from a few plants and analyzed for their isotopic ratios during the summer monsoon seasons of 2016 and 2017. The difference between the isotopic characteristics of the transpired water and rain water is expected to be nominally positive, however, a large variability was observed. This difference is found to be high (low) during the reduced (enhanced) humidity conditions and varies in tandem with the break and active phases of the monsoon season. This characteristic feature may thus be used to delineate the dry and wet phases of monsoon on local to regional scale.

Role of ultrasound guided transversus abdominis plane block as a component of multimodal analgesic regimen for lower segment caesarean section: a randomized double blind clinical study

Background

While opioids are the mainstay for post-operative analgesia after lower segment caesarean section, they are associated with various untoward effects. Ultrasound guided transversus abdominis plane (TAP) block has been postulated to provide effective analgesia for caesarean section. We evaluated the analgesic efficacy of this block for post caesarean analgesia in a randomised controlled trial.

Methods

One hundred thirty-nine mothers undergoing caesarean delivery were randomised to receive TAP block with either 20 ml 0.375% ropivacaine or 20 ml saline after obtaining informed consent. All the subjects received a standard spinal anaesthetic and diclofenac was administered for post-operative pain. Breakthrough pain was treated with tramadol. Post-operatively, all the subjects were assessed at 0, 2, 4, 6, 8, 10, 12, 18 & 24 h. The primary outcome was the time to first analgesic request. The secondary measures of outcome were pain, nausea, sedation, number of doses of tramadol administered and satisfaction with the pain management.

Results

The median (interquartile range) time to first analgesic request was prolonged in the TAP group compared to the control group (p < 0.0001); 11 h (8,12) and 4 h (2.5,6) respectively. The median (interquartile range) number of doses of tramadol consumed in the TAP group was 0 (0,1) compared to 2 (1,2) in the control group (p < 0.0001). At all points in the study, pain scores both at rest and on movement were lower in the study group (p < 0.0001). Maternal satisfaction with pain relief was also higher in the study group (p 0.0002). One subject in the TAP group had convulsions following injection of local anaesthetic solution. She was managed conservatively with supportive treatment following which she recovered.

Conclusion

TAP block reduces pain, prolongs the duration of analgesia and decreases supplemental opioid consumption when used for multimodal analgesia for pain relief after caesarean section. However, the risk of local anaesthetic systemic toxicity remains unknown with this block. Hence larger safety trials and measures to limit this complication need to be ascertained.

Trial registration

The trial was registered with the Clinical Trial Registry of India (CTRI/2017/03/008194) on 23/03/2017 (trial registered retrospectively).

Transport of Liposome Encapsulated Drugs in Voxelized Computational Model of Human Brain Tumors

There are many obstacles in the transport of chemotherapeutic drugs to tumor cells that lead to irregular and non-uniform uptake of drugs inside tumors. The study of these transport problems will help with accurate prediction of drug transport and optimizing treatment strategy. To this end, liposome mediated drug delivery has emerged as an excellent anticancer therapy due to its ability to deliver drugs at site of action and reducing the chances of side effects to the healthy tissues. In this paper, a computational fluid dynamics (CFD) model based on realistic vasculature of human brain tumor is presented. This model utilizes dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) data to account for heterogeneity in tumor vasculature. Porosity of the interstitial space inside the tumor and normal tissue is determined voxel-wise by processing the DCE-MRI images by general tracer kinetic model (GTKM). The CFD model is applied to predict transport of two different types of liposomes (stealth and conventional) in tumors. The amount of accumulated liposomes is compared with accumulated free drug (doxorubicin) in the interstitial space. Simulation results indicate that stealth liposomes accumulate more and remain for longer periods of time in tumors as compared with conventional liposomes and free drug. The present model provides us a qualitative and quantitative examination on the transport and deposition of liposomes as well as free drugs in actual human brain tumors.

Perfusion kinetics in human brain tumor with DCE-MRI derived model and CFD analysis

Cancer is one of the leading causes of death all over the world. Among the strategies that are used for cancer treatment, the effectiveness of chemotherapy is often hindered by factors such as irregular and non-uniform uptake of drugs inside tumor. Thus, accurate prediction of drug transport and deposition inside tumor is crucial for increasing the effectiveness of chemotherapeutic treatment. In this study, a computational model of human brain tumor is developed that incorporates dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) data into a voxelized porous media model. The model takes into account realistic transport and perfusion kinetics parameters together with realistic heterogeneous tumor vasculature and accurate arterial input function (AIF), which makes it patient specific. The computational results for interstitial fluid pressure (IFP), interstitial fluid velocity (IFV) and tracer concentration show good agreement with the experimental results. The computational model can be extended further for predicting the deposition of chemotherapeutic drugs in tumor environment as well as selection of the best chemotherapeutic drug for a specific patient.