Rotation Invariance and Extensive Data Augmentation: A Strategy for the MItosis DOmain Generalization (MIDOG) Challenge

Automated detection of mitotic figures in histopathology images is a challenging task: here, we present the different steps that describe the strategy we applied to participate in the MIDOG 2021 competition. The purpose of the competition was to evaluate the generalization of solutions to images acquired with unseen target scanners (hidden for the participants) under the constraint of using training data from a limited set of four independent source scanners. Given this goal and constraints, we joined the challenge by proposing a straight-forward solution based on a combination of state-of-the-art deep learning methods with the aim of yielding robustness to possible scanner-related distributional shifts at inference time. Our solution combines methods that were previously shown to be efficient for mitosis detection: hard negative mining, extensive data augmentation, rotation-invariant convolutional networks. We trained five models with different splits of the provided dataset. The subsequent classifiers produced F1-score with a mean and standard deviation of 0.747±0.032 on the test splits. The resulting ensemble constitutes our candidate algorithm: its automated evaluation on the preliminary test set of the challenge returned a F1-score of 0.6828

Towards IID representation learning and its application on biomedical data

Due to the heterogeneity of real-world data, the widely accepted independent and identically distributed (IID) assumption has been criticized in recent studies on causality. In this paper, we argue that instead of being a questionable assumption, IID is a fundamental task-relevant property that needs to be learned. Consider k independent random vectors Xi=1,…,k, we elaborate on how a variety of different causal questions can be reformulated to learning a task-relevant function ϕ that induces IID among Zi:=ϕ∘Xi, which we term IID representation learning. For proof of concept, we examine the IID representation learning on Out-of-Distribution (OOD) generalization tasks. Concretely, by utilizing the representation obtained via the learned function that induces IID, we conduct prediction of molecular characteristics (molecular prediction) on two biomedical datasets with real-world distribution shifts introduced by a) preanalytical variation and b) sampling protocol. To enable reproducibility and for comparison to the state-of-the-art (SOTA) methods, this is done by following the OOD benchmarking guidelines recommended from WILDS. Compared to the SOTA baselines supported in WILDS, the results confirm the superior performance of IID representation learning on OOD tasks. The code is publicly accessible via this https URL

Case report: Surgical repair of a large tracheo-esophageal fistula in a patient with post-transplant esophageal lymphoproliferative disorder

Introduction and importance The management of large malignant tracheo-esophageal fistulas (TEF) is not standardized. Herein, we report a case with a malignant TEF associated with esophageal post-transplant lymphoproliferative disorder (PTLD) for whom we successfully performed a surgical repair. This contributes to the knowledge on how to treat large acquired malignant TEFs. Case presentation A 69-year old male presented with a one-week history of fever, productive cough and bilateral coarse crackles. In addition, he described a weight loss of 10 kg during the past three months. The patient's history included a kidney transplantation twenty years ago. Esophagogastroduodenoscopy with a biopsy of the esophagus was performed nine days before. Histopathology showed a PTLD of diffuse large B-cell lymphoma subtype. Subsequent diagnostics revealed a progressive TEF (approx. 2.0 × 1.5 cm) 3.0 cm above the carina. PET-CT scan showed an esophagus with slight tracer uptake in the middle third (approx. 11.5 cm length, SUV max 7.4). After decision against stenting, transthoracic subtotal esophagectomy with closure of the tracheal mouth of the fistula by a pedicled flap was performed. PTLD was treated with prednisone and rituximab. Tumor progression (brain metastasis) led to death 95 days after surgery. Clinical discussion The treatment of a malignant TEF is complex and personalized while both the consequences of the esophago-tracheal connection and those of the underlying responsible diagnosis have to be considered concurrently. In this case, we considered surgery as the best treatment option due to a relatively good prognosis of the underlying diagnosis (PTLD) and a large fistula. Esophageal or dual stenting, the treatment of choice for small malignant TEF, would have been associated with a high risk of failure due to the wide trachea, extensively dilated esophagus, proximal location and large diameter of the fistula. Conclusion Surgery can be considered for patients with a large acquired malignant TEF and positive long-term prognosis of the underlying diagnosis. Due to the complexity of TEF management, immediate pre-operative multidisciplinary discussion is advised

Automated causal inference in application to randomized controlled clinical trials

Randomized controlled trials (RCTs) are considered the gold standard for testing causal hypotheses in the clinical domain; however, the investigation of prognostic variables of patient outcome in a hypothesized cause–effect route is not feasible using standard statistical methods. Here we propose a new automated causal inference method (AutoCI) built on the invariant causal prediction (ICP) framework for the causal reinterpretation of clinical trial data. Compared with existing methods, we show that the proposed AutoCI allows one to clearly determine the causal variables of two real-world RCTs of patients with endometrial cancer with mature outcome and extensive clinicopathological and molecular data. This is achieved via suppressing the causal probability of non-causal variables by a wide margin. In ablation studies, we further demonstrate that the assignment of causal probabilities by AutoCI remains consistent in the presence of confounders. In conclusion, these results confirm the robustness and feasibility of AutoCI for future applications in real-world clinical analysis

a retrospective cohort study

Background Metastasis of colorectal cancer (CRC) is directly linked to patient survival. We previously identified the novel gene Metastasis Associated in Colon Cancer 1 (MACC1) in CRC and demonstrated its importance as metastasis inducer and prognostic biomarker. Here, we investigate the geographic expression pattern of MACC1 in colorectal adenocarcinoma and tumor buds in correlation with clinicopathological and molecular features for improvement of survival prognosis. Methods We performed geographic MACC1 expression analysis in tumor center, invasive front and tumor buds on whole tissue sections of 187 well-characterized CRCs by immunohistochemistry. MACC1 expression in each geographic zone was analyzed with Mismatch repair (MMR)-status, BRAF/KRAS- mutations and CpG-island methylation. Results MACC1 was significantly overexpressed in tumor tissue as compared to normal mucosa (p < 0.001). Within colorectal adenocarcinomas, a significant increase of MACC1 from tumor center to front (p = 0.0012) was detected. MACC1 was highly overexpressed in 55% tumor budding cells. Independent of geographic location, MACC1 predicted advanced pT and pN-stages, high grade tumor budding, venous and lymphatic invasion (p < 0.05). High MACC1 expression at the invasive front was decisive for prediction of metastasis (p = 0.0223) and poor survival (p = 0.0217). The geographic pattern of MACC1 did not correlate with MMR-status, BRAF/KRAS- mutations or CpG-island methylation. Conclusion MACC1 is differentially expressed in CRC. At the invasive front, MACC1 expression predicts best aggressive clinicopathological features, tumor budding, metastasis formation and poor survival outcome

Combinational expression of tumor testis antigens NY-ESO-1, MAGE-A3, and MAGE-A4 predicts response to immunotherapy in mucosal melanoma patients

PURPOSE: Immunotherapy using immune checkpoint inhibitors (ICI) has revolutionized cancer treatment in recent years, particularly in melanoma. While response to immunotherapy is associated with high tumor mutational burden (TMB), PD-L1 expression, and microsatellite instability in several cancers, tumors lacking these biomarkers can still respond to this treatment. Especially, mucosal melanoma, commonly exhibiting low TMB compared to cutaneous melanoma, may respond to immunotherapy with immune checkpoint inhibitors. Therefore, the aim of our study was to investigate novel biomarkers in mucosal melanoma that predict response to combined ipilimumab and nivolumab. METHODS: We investigated 10 tumor samples from 10 patients (three responders, seven non-responders) before treatment and six tumor samples from five patients after progression using a targeted Next Generation Sequencing (NGS) gene expression panel. The findings were corroborated with an independent method (i.e., immunohistochemical staining) on the same 10 tumor samples before treatment and, to increase the cohort, in addition on three tumor samples before treatment of more recent patients (one responder, two non-responders). RESULTS: With the targeted gene expression panel, we found the three tumor testis antigens CTAG1B (NY-ESO-1), MAGE-A3, and MAGE-A4 to be predominantly expressed in responding tumors. This marker panel was either not or not completely expressed in non-responders (p < 0.01). Using immunohistochemistry for all three markers, we could confirm the elevated expression in tumors responding to the ipilimumab/nivolumab combination therapy. CONCLUSION: In conclusion, these three biomarkers await validation in a larger patient cohort and could be easily used in future routine diagnostics to predict the outcome of ipilimumab/nivolumab combination therapy in mucosal melanoma patients

Integrated Analysis Of Immunotherapy Treated Clear Cell Renal Cell Carcinomas: An Exploratory Study

Molecular or immunological differences between responders and nonresponders to immune checkpoint inhibitors (ICIs) of clear cell renal cell carcinomas (ccRCCs) remain incompletely understood. To address this question, we performed next-generation sequencing, methylation analysis, genome wide copy number analysis, targeted RNA sequencing and T-cell receptor sequencing, and we studied frequencies of tumor-infiltrating CD8+ T cells, presence of tertiary lymphoid structures (TLS) and PD-L1 expression in 8 treatment-naive ccRCC patients subsequently treated with ICI (3 responders, 5 nonresponders). Unexpectedly, we identified decreased frequencies of CD8+ tumor-infiltrating T cells and TLS, and a decreased expression of PD-L1 in ICI responders when compared with nonresponders. However, neither tumor-specific genetic alterations nor gene expression profiles correlated with response to ICI or the observed immune features. Our results underline the challenge to stratify ccRCC patients for immunotherapy based on routinely available pathologic primary tumor material, even with advanced technologies. Our findings emphasize the analysis of pretreated metastatic tissue in line with recent observations describing treatment effects on the tumor microenvironment. In addition, our data call for further investigation of additional parameters in a larger ccRCC cohort to understand the mechanistic implications of the observed differences in tumor-infiltrating CD8+ T cells, TLS, and PD-L1 expression

Single-Cell Quantification of mRNA Expression in The Human Brain

RNA analysis at the cellular resolution in the human brain is challenging. Here, we describe an optimised approach for detecting single RNA transcripts in a cell-type specific manner in frozen human brain tissue using multiplexed fluorescent RNAscope probes. We developed a new robust analytical approach for RNAscope quantification. Our method shows that low RNA integrity does not significantly affect RNAscope signal, recapitulates bulk RNA analysis and provides spatial context to transcriptomic analysis of human post-mortem brain at single-cell resolution. In summary, our optimised method allows the usage of frozen human samples from brain banks to perform quantitative RNAscope analysis

Automated causal inference in application to randomized controlled clinical trials

Randomized controlled trials (RCTs) are considered the gold standard for testing causal hypotheses in the clinical domain; however, the investigation of prognostic variables of patient outcome in a hypothesized cause–effect route is not feasible using standard statistical methods. Here we propose a new automated causal inference method (AutoCI) built on the invariant causal prediction (ICP) framework for the causal reinterpretation of clinical trial data. Compared with existing methods, we show that the proposed AutoCI allows one to clearly determine the causal variables of two real-world RCTs of patients with endometrial cancer with mature outcome and extensive clinicopathological and molecular data. This is achieved via suppressing the causal probability of non-causal variables by a wide margin. In ablation studies, we further demonstrate that the assignment of causal probabilities by AutoCI remains consistent in the presence of confounders. In conclusion, these results confirm the robustness and feasibility of AutoCI for future applications in real-world clinical analysis