Predicting patient outcomes after treatment with immune checkpoint blockade: A review of biomarkers derived from diverse data modalities.

Immune checkpoint blockade (ICB) therapy targeting cytotoxic T-lymphocyte-associated protein 4, programmed death 1, and programmed death ligand 1 has shown durable remission and clinical success across different cancer types. However, patient outcomes vary among disease indications. Studies have identified prognostic biomarkers associated with immunotherapy response and patient outcomes derived from diverse data types, including next-generation bulk and single-cell DNA, RNA, T cell and B cell receptor sequencing data, liquid biopsies, and clinical imaging. Owing to inter- and intra-tumor heterogeneity and the immune system's complexity, these biomarkers have diverse efficacy in clinical trials of ICB. Here, we review the genetic and genomic signatures and image features of ICB studies for pan-cancer applications and specific indications. We discuss the advantages and disadvantages of computational approaches for predicting immunotherapy effectiveness and patient outcomes. We also elucidate the challenges of immunotherapy prognostication and the discovery of novel immunotherapy targets.

Peripheral blood TCR clonotype diversity as an age-associated marker of breast cancer progression.

Immune escape is a prerequisite for tumor growth. We previously described a decline in intratumor activated cytotoxic T cells and T cell receptor (TCR) clonotype diversity in invasive breast carcinomas compared to ductal carcinoma in situ (DCIS), implying a central role of decreasing T cell responses in tumor progression. To determine potential associations between peripheral immunity and breast tumor progression, here, we assessed the peripheral blood TCR clonotype of 485 breast cancer patients diagnosed with either DCIS or de novo stage IV disease at younger (<45) or older (≥45) age. TCR clonotype diversity was significantly lower in older compared to younger breast cancer patients regardless of tumor stage at diagnosis. In the younger age group, TCR-α clonotype diversity was lower in patients diagnosed with de novo stage IV breast cancer compared to those diagnosed with DCIS. In the older age group, DCIS patients with higher TCR-α clonotype diversity were more likely to have a recurrence compared to those with lower diversity. Whole blood transcriptome profiles were distinct depending on the TCR-α Chao1 diversity score. There were more CD8+ T cells and a more active immune environment in DCIS tumors of young patients with higher peripheral blood TCR-α Chao1 diversity than in those with lower diversity. These results provide insights into the role that host immunity plays in breast cancer development across different age groups.

Tutorial: integrative computational analysis of bulk RNA-sequencing data to characterize tumor immunity using RIMA.

RNA-sequencing (RNA-seq) has become an increasingly cost-effective technique for molecular profiling and immune characterization of tumors. In the past decade, many computational tools have been developed to characterize tumor immunity from gene expression data. However, the analysis of large-scale RNA-seq data requires bioinformatics proficiency, large computational resources and cancer genomics and immunology knowledge. In this tutorial, we provide an overview of computational analysis of bulk RNA-seq data for immune characterization of tumors and introduce commonly used computational tools with relevance to cancer immunology and immunotherapy. These tools have diverse functions such as evaluation of expression signatures, estimation of immune infiltration, inference of the immune repertoire, prediction of immunotherapy response, neoantigen detection and microbiome quantification. We describe the RNA-seq IMmune Analysis (RIMA) pipeline integrating many of these tools to streamline RNA-seq analysis. We also developed a comprehensive and user-friendly guide in the form of a GitBook with text and video demos to assist users in analyzing bulk RNA-seq data for immune characterization at both individual sample and cohort levels by using RIMA.

Private Practice Radiologist Subspecialty Classification Using Medicare Claims.

PURPOSE: The aim of this study was to assess both existing Medicare provider code assignments and a new claims-based system for subspecialty classification of private practice radiologists. METHODS: Websites of the 100 largest US radiology private practices were used to identify 1,476 radiologists self-identified with a single subspecialty ([1] abdominal, [2] breast, [3] cardiothoracic, or [4] musculoskeletal imaging; [5] nuclear medicine; [6] interventional radiology; [7] neuroradiology). Concordance of existing Medicare radiology subspecialty provider codes (present only for nuclear medicine and interventional radiology) was first assessed. Next, using a classification approach based on Neiman Imaging Types of Service (NITOS) piloted among academic practices, the percentage of subspecialty work relative value units (wRVUs) from 2012 to 2014 Medicare claims were used to assign each radiologist a unique subspecialty. RESULTS: Existing Medicare provider codes matched only 8.0% of nuclear medicine physicians and 10.7% of interventional radiologists to their self-reported subspecialties. The NITOS-based system mapped a median 51.9% of private practice radiologists' wRVUs to self-identified subspecialties (range, 23.3% [nuclear medicine] to 73.6% [neuroradiology]). The 50% NITOS-based wRVU threshold previously established for academic radiologists correctly assigned subspecialties to 48.8% of private practice radiologists but incorrectly categorized 2.9%. Practice patterns of the remaining 48.3% were sufficiently varied such that no single subspecialty assignment was possible. CONCLUSIONS: Existing Medicare provider codes poorly mirror subspecialty radiologists' own practice website-designated subspecialties. Actual payer claims data permit far more granular and accurate subspecialty identification for many radiologists. As new payment models increasingly focus on subspecialty-specific performance measures, claims-based identification methodologies show promise for reproducibly and transparently matching radiologists to practice-relevant metrics.