Concordance of Whole-Slide Imaging and Conventional Light Microscopy for Assessment of Pathologic Response Following Neoadjuvant Therapy for Lung Cancer.

Pathologic response is an endpoint in many ongoing clinical trials for neoadjuvant regimens, including immune checkpoint blockade and chemotherapy. Whole-slide scanning of glass slides generates high-resolution digital images and allows for remote review and potential measurement with image analysis tools, but concordance of pathologic response assessment on digital scans compared with that on glass slides has yet to be evaluated. Such a validation goes beyond previous concordance studies, which focused on establishing surgical pathology diagnoses, as it requires quantitative assessment of tumor, necrosis, and regression.

Evaluation of a Task-Specific Self-Supervised Learning Framework in Digital Pathology Relative to Transfer Learning Approaches and Existing Foundation Models.

An integral stage in typical digital pathology workflows involves deriving specific features from tiles extracted from a tessellated whole-slide image. Notably, various computer vision neural network architectures, particularly the ImageNet pretrained, have been extensively used in this domain. This study critically analyzes multiple strategies for encoding tiles to understand the extent of transfer learning and identify the most effective approach.

Computational methods and biomarker discovery strategies for spatial proteomics: a review in immuno-oncology.

Advancements in imaging technologies have revolutionized our ability to deeply profile pathological tissue architectures, generating large volumes of imaging data with unparalleled spatial resolution. This type of data collection, namely, spatial proteomics, offers invaluable insights into various human diseases. Simultaneously, computational algorithms have evolved to manage the increasing dimensionality of spatial proteomics inherent in this progress.

Analysis of 3D pathology samples using weakly supervised AI.

Human tissue, which is inherently three-dimensional (3D), is traditionally examined through standard-of-care histopathology as limited two-dimensional (2D) cross-sections that can insufficiently represent the tissue due to sampling bias. To holistically characterize histomorphology, 3D imaging modalities have been developed, but clinical translation is hampered by complex manual evaluation and lack of computational platforms to distill clinical insights from large, high-resolution datasets. We present TriPath, a deep-learning platform for processing tissue volumes and efficiently predicting clinical outcomes based on 3D morphological features.

Multiple-instance learning of somatic mutations for the classification of tumour type and the prediction of microsatellite status.

Large-scale genomic data are well suited to analysis by deep learning algorithms. However, for many genomic datasets, labels are at the level of the sample rather than for individual genomic measures. Machine learning models leveraging these datasets generate predictions by using statically encoded measures that are then aggregated at the sample level.

Probabilistic Mixture Models Improve Calibration of Panel-derived Tumor Mutational Burden in the Context of both Tumor-normal and Tumor-only Sequencing.

Background: Tumor mutational burden (TMB) has been investigated as a biomarker for immune checkpoint blockade (ICB) therapy. Increasingly, TMB is being estimated with gene panel-based assays (as opposed to full exome sequencing) and different gene panels cover overlapping but distinct genomic coordinates, making comparisons across panels difficult. Previous studies have suggested that standardization and calibration to exome-derived TMB be done for each panel to ensure comparability.

Predictive models of response to neoadjuvant chemotherapy in muscle-invasive bladder cancer using nuclear morphology and tissue architecture.

Characterizing likelihood of response to neoadjuvant chemotherapy (NAC) in muscle-invasive bladder cancer (MIBC) is an important yet unmet challenge. In this study, a machine-learning framework is developed using imaging of biopsy pathology specimens to generate models of likelihood of NAC response. Developed using cross-validation (evaluable N = 66) and an independent validation cohort (evaluable N = 56), our models achieve promising results (65%-73% accuracy).

Deep learning identifies antigenic determinants of severe SARS-CoV-2 infection within T-cell repertoires.

SARS-CoV-2 infection is characterized by a highly variable clinical course with patients experiencing asymptomatic infection all the way to requiring critical care support. This variation in clinical course has led physicians and scientists to study factors that may predispose certain individuals to more severe clinical presentations in hopes of either identifying these individuals early in their illness or improving their medical management. We sought to understand immunogenomic differences that may result in varied clinical outcomes through analysis of T-cell receptor sequencing (TCR-Seq) data in the open access ImmuneCODE database.

Deep learning for diagnosis of acute promyelocytic leukemia via recognition of genomically imprinted morphologic features.

Acute promyelocytic leukemia (APL) is a subtype of acute myeloid leukemia (AML), classified by a translocation between chromosomes 15 and 17 [t(15;17)], that is considered a true oncologic emergency though appropriate therapy is considered curative. Therapy is often initiated on clinical suspicion, informed by both clinical presentation as well as direct visualization of the peripheral smear. We hypothesized that genomic imprinting of morphologic features learned by deep learning pattern recognition would have greater discriminatory power and consistency compared to humans, thereby facilitating identification of t(15;17) positive APL.

DeepTCR is a deep learning framework for revealing sequence concepts within T-cell repertoires.

Deep learning algorithms have been utilized to achieve enhanced performance in pattern-recognition tasks. The ability to learn complex patterns in data has tremendous implications in immunogenomics. T-cell receptor (TCR) sequencing assesses the diversity of the adaptive immune system and allows for modeling its sequence determinants of antigenicity.

Integrative Tumor and Immune Cell Multi-omic Analyses Predict Response to Immune Checkpoint Blockade in Melanoma.

In this study, we incorporate analyses of genome-wide sequence and structural alterations with pre- and on-therapy transcriptomic and T cell repertoire features in immunotherapy-naive melanoma patients treated with immune checkpoint blockade. Although tumor mutation burden is associated with improved treatment response, the mutation frequency in expressed genes is superior in predicting outcome. Increased T cell density in baseline tumors and dynamic changes in regression or expansion of the T cell repertoire during therapy distinguish responders from non-responders.

Multimodal genomic features predict outcome of immune checkpoint blockade in non-small-cell lung cancer.

Despite progress in immunotherapy, identifying patients that respond has remained a challenge. Through analysis of whole-exome and targeted sequence data from 5,449 tumors, we found a significant correlation between tumor mutation burden (TMB) and tumor purity, suggesting that low tumor purity tumors are likely to have inaccurate TMB estimates. We developed a new method to estimate a corrected TMB (cTMB) that was adjusted for tumor purity and more accurately predicted outcome to immune checkpoint blockade (ICB).

Estrogen receptor-β signaling induces cisplatin resistance in bladder cancer.

The efficacy of cisplatin-based chemotherapy in patients with bladder cancer is often limited due to the development of therapeutic resistance. Our recent findings in bladder cancer suggested that activation of prostaglandin receptors ( EP2, EP4) or cyclooxygenase (COX)-2 induced cisplatin resistance. Meanwhile, emerging evidence indicates the involvement of estrogen receptor-β (ERβ) signals in urothelial cancer progression.

FOXO1 inactivation induces cisplatin resistance in bladder cancer.

We found that FOXO1-shRNA sublines or FOXO1-positive cells co-treated with a FOXO1 inhibitor were significantly more resistant to cisplatin treatment at pharmacological concentrations, compared with respective control sublines or those with mock treatment. Western blot demonstrated considerable increases in the expression levels of a phosphorylated inactive form of FOXO1 (p-FOXO1) in cisplatin-resistant sublines established by long-term culture with low/increasing doses of cisplatin, compared with respective controls. Immunohistochemistry in surgical specimens from patients with muscle-invasive bladder cancer undergoing cisplatin-based neoadjuvant therapy further showed a strong trend to associate between p-FOXO1 positivity and unfavorable response to chemotherapy.

FOXO1 as a tumor suppressor inactivated via AR/ERβ signals in urothelial cells.

Androgen receptor (AR) and estrogen receptor-β (ERβ) have been implicated in urothelial tumor outgrowth as promoters, while underlying mechanisms remain poorly understood. Our transcription factor profiling previously performed identified FOXO1 as a potential downstream target of AR in bladder cancer cells. We here investigated the functional role of FOXO1 in the development and progression of urothelial cancer in relation to AR and ERβ signals.

Deletions are Associated with Metastasis in African American Men with Clinically Localized, Surgically Treated Prostate Cancer.

Purpose: The potential biological determinants of aggressive prostate cancer in African American (AA) men are unknown. Here we characterize prostate cancer genomic alterations in the largest cohort to date of AA men with clinical follow-up for metastasis, with the aim to elucidate the key molecular drivers associated with poor prognosis in this population.

Experimental Design: Targeted sequencing was retrospectively performed on 205 prostate tumors from AA men treated with radical prostatectomy (RP) to examine somatic genomic alterations and percent of the genome with copy-number alterations (PGA).

Reporting Practices and Resource Utilization in the Era of Intraductal Carcinoma of the Prostate: A Survey of Genitourinary Subspecialists.

Intraductal carcinoma of the prostate (IDC-P) has been recently recognized by the World Health Organization classification of prostatic tumors as a distinct entity, most often occurring concurrently with invasive prostatic adenocarcinoma (PCa). Whether documented admixed with PCa or in its rare pure form, numerous studies associate this entity with clinical aggressiveness. Despite increasing clinical experience and requirement of IDC-P documentation in protocols for synoptic reporting, the specifics of its potential contribution to assessment of grade group (GG) and cancer quantitation of PCa in both needle biopsies (NBx) and radical prostatectomy (RP) specimens remain unclear.