Avelumab first-line maintenance in advanced urothelial carcinoma: Complete screening for prognostic and predictive factors using machine learning in the JAVELIN Bladder 100 phase 3 trial.

BACKGROUND: Avelumab first-line (1 L) maintenance is a standard of care for advanced urothelial carcinoma (aUC) based on the JAVELIN Bladder 100 phase 3 trial, which showed that avelumab 1 L maintenance + best supportive care (BSC) significantly prolonged overall survival (OS) and progression-free survival (PFS) vs BSC alone in patients who were progression free after receiving 1 L platinum-containing chemotherapy. Here, we comprehensively screened JAVELIN Bladder 100 trial datasets to identify prognostic factors that define subpopulations of patients with longer or shorter OS irrespective of treatment, and predictive factors that select patients who could obtain a greater OS benefit from avelumab 1 L maintenance treatment. METHODS: We performed machine learning analyses to screen a large set of baseline covariates, including patient demographics, disease characteristics, laboratory values, molecular biomarkers, and patient-reported outcomes. Covariates were identified from previously reported analyses and established prognostic and predictive markers. Variables selected from random survival forest models were processed further in univariate Cox models with treatment interaction and visually inspected using correlation analysis and Kaplan-Meier curves. Results were summarized in a multivariable Cox model. RESULTS: Prognostic baseline covariates associated with OS included in the final model were assignment to avelumab 1 L maintenance treatment, Eastern Cooperative Oncology Group performance status, site of metastasis, sum of longest target lesion diameters, levels of C-reactive protein and alkaline phosphatase in blood, lymphocyte proportion in intratumoral stroma, tumor mutational burden, and tumor CD8+ T-cell infiltration. Potential predictive factors included site of metastasis, tumor mutation burden, and tumor CD8+ T-cell infiltration. An analysis in patients with PD-L1+ tumors had similar findings to those in the overall population. CONCLUSIONS: Machine learning analyses of data from the JAVELIN Bladder 100 trial identified potential prognostic and predictive factors for avelumab 1 L maintenance treatment in patients with aUC, which warrant further evaluation in other clinical datasets.

Tumor growth inhibition modeling in patients with second line biliary tract cancer and first line non-small cell lung cancer based on bintrafusp alfa trials.

This analysis aimed to quantify tumor dynamics in patients receiving either bintrafusp alfa (BA) or pembrolizumab, by population pharmacokinetic (PK)-pharmacodynamic modeling, and investigate clinical and molecular covariates describing the variability in tumor dynamics by pharmacometric and machine-learning (ML) approaches. Data originated from two clinical trials in patients with biliary tract cancer (BTC; NCT03833661) receiving BA and non-small cell lung cancer (NSCLC; NCT03631706) receiving BA or pembrolizumab. Individual drug exposure was estimated from previously developed population PK models. Population tumor dynamics models were developed for each drug-indication combination, and covariate evaluations performed using nonlinear mixed-effects modeling (NLME) and ML (elastic net and random forest models) approaches. The three tumor dynamics' model structures all included linear tumor growth components and exponential tumor shrinkage. The final BTC model included the effect of drug exposure (area under the curve) and several covariates (demographics, disease-related, and genetic mutations). Drug exposure was not significant in either of the NSCLC models, which included two, disease-related, covariates in the BA arm, and none in the pembrolizumab arm. The covariates identified by univariable NLME and ML highly overlapped in BTC but showed less agreement in NSCLC analyses. Hyperprogression could be identified by higher tumor growth and lower tumor kill rates and could not be related to BA exposure. Tumor size over time was quantitatively characterized in two tumor types and under two treatments. Factors potentially related to tumor dynamics were assessed using NLME and ML approaches; however, their net impact on tumor size was considered as not clinically relevant.

Pan-tumor T-lymphocyte detection using deep neural networks: Recommendations for transfer learning in immunohistochemistry.

The success of immuno-oncology treatments promises long-term cancer remission for an increasing number of patients. The response to checkpoint inhibitor drugs has shown a correlation with the presence of immune cells in the tumor and tumor microenvironment. An in-depth understanding of the spatial localization of immune cells is therefore critical for understanding the tumor's immune landscape and predicting drug response. Computer-aided systems are well suited for efficiently quantifying immune cells in their spatial context. Conventional image analysis approaches are often based on color features and therefore require a high level of manual interaction. More robust image analysis methods based on deep learning are expected to decrease this reliance on human interaction and improve the reproducibility of immune cell scoring. However, these methods require sufficient training data and previous work has reported low robustness of these algorithms when they are tested on out-of-distribution data from different pathology labs or samples from different organs. In this work, we used a new image analysis pipeline to explicitly evaluate the robustness of marker-labeled lymphocyte quantification algorithms depending on the number of training samples before and after being transferred to a new tumor indication. For these experiments, we adapted the RetinaNet architecture for the task of T-lymphocyte detection and employed transfer learning to bridge the domain gap between tumor indications and reduce the annotation costs for unseen domains. On our test set, we achieved human-level performance for almost all tumor indications with an average precision of 0.74 in-domain and 0.72-0.74 cross-domain. From our results, we derive recommendations for model development regarding annotation extent, training sample selection, and label extraction for the development of robust algorithms for immune cell scoring. By extending the task of marker-labeled lymphocyte quantification to a multi-class detection task, the pre-requisite for subsequent analyses, e.g., distinguishing lymphocytes in the tumor stroma from tumor-infiltrating lymphocytes, is met.

14-3-3 proteins in membrane protein transport.

14-3-3 proteins affect the cell surface expression of several unrelated cargo membrane proteins, e.g., MHC II invariant chain, the two-pore potassium channels KCNK3 and KCNK9, and a number of different reporter proteins exposing Arg-based endoplasmic reticulum localization signals in mammalian and yeast cells. These multimeric membrane proteins have a common feature in that they all expose coatomer protein complex I (COPI)- and 14-3-3-binding motifs. 14-3-3 binding depends on phosphorylation of the membrane protein in some and on multimerization of the membrane protein in other cases. Evidence from mutant proteins that are unable to interact with either COPI or 14-3-3 and from yeast cells with an altered 14-3-3 content suggests that 14-3-3 proteins affect forward transport in the secretory pathway. Mechanistically, this could be explained by clamping, masking, or scaffolding. In the clamping mechanism, 14-3-3 binding alters the conformation of the signal-exposing tail of the membrane protein, whereas masking or scaffolding would abolish or allow the interaction of the membrane protein with other proteins or complexes. Interaction partners identified as putative 14-3-3 binding partners in affinity purification approaches constitute a pool of candidate proteins for downstream effectors, such as coat components, coat recruitment GTPases, Rab GTPases, GTPase-activating proteins (GAPs), guanine-nucleotide exchange factors (GEFs) and motor proteins.

A multimeric membrane protein reveals 14-3-3 isoform specificity in forward transport in yeast.

Arginine (Arg)-based endoplasmic reticulum (ER) localization signals are sorting motifs involved in the quality control of multimeric membrane proteins. They are distinct from other ER localization signals like the C-terminal di-lysine [-K(X)KXX] signal. The Pmp2p isoproteolipid, a type I yeast membrane protein, reports faithfully on the activity of sorting signals when fused to a tail containing either an Arg-based motif or a -KKXX signal. This reporter reveals that the Arg-based ER localization signals from mammalian Kir6.2 and GB1 proteins are functional in yeast. Thus, the machinery involved in recognition of Arg-based signals is evolutionarily conserved. Multimeric presentation of the Arg-based signal from Kir6.2 on Pmp2p results in forward transport, which requires 14-3-3 proteins encoded in yeast by BMH1 and BMH2 in two isoforms. Comparison of a strain without any 14-3-3 proteins (Deltabmh2) and the individual Deltabmh1 or Deltabmh2 shows that the role of 14-3-3 in the trafficking of this multimeric Pmp2p reporter is isoform-specific. Efficient forward transport requires the presence of Bmh1p. The specific role of Bmh1p is not due to differences in abundance or affinity between the isoforms. Our results imply that 14-3-3 proteins mediate forward transport by a mechanism distinct from simple masking of the Arg-based signal.