Classification of non-TCGA cancer samples to TCGA molecular subtypes using compact feature sets.

Molecular subtypes, such as defined by The Cancer Genome Atlas (TCGA), delineate a cancer's underlying biology, bringing hope to inform a patient's prognosis and treatment plan. However, most approaches used in the discovery of subtypes are not suitable for assigning subtype labels to new cancer specimens from other studies or clinical trials. Here, we address this barrier by applying five different machine learning approaches to multi-omic data from 8,791 TCGA tumor samples comprising 106 subtypes from 26 different cancer cohorts to build models based upon small numbers of features that can classify new samples into previously defined TCGA molecular subtypes-a step toward molecular subtype application in the clinic.

Prognostic value of residual disease (RD) biology and gene expression changes during the neoadjuvant treatment in patients with HER2+ early breast cancer (EBC).

Background: In HER2+ early breast cancer (EBC), we investigated tumor and immune changes during neoadjuvant treatment and their impact on residual disease (RD) biology and prognostic implications across 4 neoadjuvant studies of trastuzumab with or without lapatinib, and with or without chemotherapy: CALGB 40601, PAMELA, NeoALTTO and NSABP B-41.

Patients And Methods: We compared tumor and immune gene expression changes during neoadjuvant treatment and their association with with event-free survival (EFS) by uni- and multivariable Cox regression models in different cohorts and timepoints: 452 RD samples at baseline including 169 with a paired RD, and biomarker changes during neoadjuvant therapy, evaluating model performance via the c-index.

Results: Analysis of 169 paired tumor samples revealed a shift in intrinsic subtype proportions from HER2-Enriched at baseline (50.

DAB2IP loss in luminal a breast cancer leads to NF-κB-associated aggressive oncogenic phenotypes.

Despite proven therapy options for estrogen receptor-positive (ER+) breast tumors, a substantial number of patients with ER+ breast cancer exhibit relapse with associated metastasis. Loss of expression of RasGAPs leads to poor outcomes in several cancers, including breast cancer. Mining the The Cancer Genome Atlas (TCGA) breast cancer RNA-Seq dataset revealed that low expression of the RasGAP DAB2IP was associated with a significant decrease in relapse-free survival in patients with Luminal A breast cancer.

Measure of Strength of Evidence for Visually Observed Differences between Subpopulations.

For measuring the strength of visually-observed subpopulation differences, the Population Difference Criterion is proposed to assess the statistical significance of visually observed subpopulation differences. It addresses the following challenges: in high-dimensional contexts, distributional models can be dubious; in high-signal contexts, conventional permutation tests give poor pairwise comparisons. We also make two other contributions: Based on a careful analysis we find that a balanced permutation approach is more powerful in high-signal contexts than conventional permutations.

Differences in 21-Gene and PAM50 Recurrence Scores in Younger and Black Women With Breast Cancer.

Purpose: Genomic tests, such as the Oncotype Dx 21-gene and Prosigna risk of recurrence (ROR-P) assay, are commonly used for breast cancer prognostication. Emerging data suggest variability between assays, but this has not been compared in diverse populations.

Materials And Methods: RNA sequencing was performed on 647 previously untreated stage I-III estrogen receptor-positive/human epidermal growth factor receptor 2-negative tumors in the Carolina Breast Cancer Study, which oversampled Black and younger women (age <50 years at diagnosis), using research versions of two common RNA-based prognostic assays: ROR-P and the 21-gene recurrence score (RS).

Visual Intratumor Heterogeneity and Breast Tumor Progression.

High intratumoral heterogeneity is thought to be a poor prognostic indicator. However, the source of heterogeneity may also be important, as genomic heterogeneity is not always reflected in histologic or 'visual' heterogeneity. We aimed to develop a predictor of histologic heterogeneity and evaluate its association with outcomes and molecular heterogeneity.

Harmonizing heterogeneous transcriptomics datasets for machine learning-based analysis to identify spaceflown murine liver-specific changes.

NASA has employed high-throughput molecular assays to identify sub-cellular changes impacting human physiology during spaceflight. Machine learning (ML) methods hold the promise to improve our ability to identify important signals within highly dimensional molecular data. However, the inherent limitation of study subject numbers within a spaceflight mission minimizes the utility of ML approaches.

Tumor Intrinsic Subtypes and Gene Expression Signatures in Early-Stage ERBB2/HER2-Positive Breast Cancer: A Pooled Analysis of CALGB 40601, NeoALTTO, and NSABP B-41 Trials.

Importance: Biologic features may affect pathologic complete response (pCR) and event-free survival (EFS) after neoadjuvant chemotherapy plus ERBB2/HER2 blockade in ERBB2/HER2-positive early breast cancer (EBC).

Objective: To define the quantitative association between pCR and EFS by intrinsic subtype and by other gene expression signatures in a pooled analysis of 3 phase 3 trials: CALGB 40601, NeoALTTO, and NSABP B-41.

Design, Setting, And Participants: In this retrospective pooled analysis, 1289 patients with EBC received chemotherapy plus either trastuzumab, lapatinib, or the combination, with a combined median follow-up of 5.

Diffsig: Associating Risk Factors with Mutational Signatures.

Background: Somatic mutational signatures elucidate molecular vulnerabilities to therapy, and therefore detecting signatures and classifying tumors with respect to signatures has clinical value. However, identifying the etiology of the mutational signatures remains a statistical challenge, with both small sample sizes and high variability in classification algorithms posing barriers. As a result, few signatures have been strongly linked to particular risk factors.

Association of PIK3CA Mutation With Pathologic Complete Response and Outcome by Hormone Receptor Status and Intrinsic Subtype in Early-Stage ERBB2/HER2-Positive Breast Cancer.

Importance: PIK3CA mutations may be associated with outcomes of patients with ERBB2/HER2-positive early breast cancer (EBC).

Objectives: To assess if PIK3CA mutations among patients with ERBB2/HER2-positive EBC are associated with treatment response and outcome, and if these associations vary by hormone receptor (HR) status or intrinsic molecular subtype (IMS).

Design, Setting, And Participants: This cohort study derived data on 184 patients from the phase 3 neoadjuvant Cancer and Leukemia Group B (CALGB) 40601 trial that enrolled patients with ERBB2/HER2-positive EBC in North America between January 1, 2008, and December 31, 2012.

PAIRWISE NONLINEAR DEPENDENCE ANALYSIS OF GENOMIC DATA.

In The Cancer Genome Atlas (TCGA) data set, there are many interesting nonlinear dependencies between pairs of genes that reveal important relationships and subtypes of cancer. Such genomic data analysis requires a rapid, powerful and interpretable detection process, especially in a high-dimensional environment. We study the nonlinear patterns among the expression of pairs of genes from TCGA using a powerful tool called Binary Expansion Testing.

Expanded detection and impact of alterations in cancer.

Aberrant expression of the tumor suppressor gene is a prominent risk factor for several tumor types and is important in tumor evolution and progression. Here we performed integrated multi-omic analyses using data from The Cancer Genome Atlas (TCGA) for 33 cancer types and over 10,000 individuals to identify alterations leading to disruption. We combined existing variant calls and new calls derived from a local realignment pipeline across multiple independent variant callers, increasing somatic variant detection by 41% from 182 to 257, including 11 indels ≥40bp.

Predicting early breast cancer recurrence from histopathological images in the Carolina Breast Cancer Study.

Approaches for rapidly identifying patients at high risk of early breast cancer recurrence are needed. Image-based methods for prescreening hematoxylin and eosin (H&E) stained tumor slides could offer temporal and financial efficiency. We evaluated a data set of 704 1-mm tumor core H&E images (2-4 cores per case), corresponding to 202 participants (101 who recurred; 101 non-recurrent matched on age and follow-up time) from breast cancers diagnosed between 2008-2012 in the Carolina Breast Cancer Study.

Immunological and clinicopathological features predict HER2-positive breast cancer prognosis in the neoadjuvant NeoALTTO and CALGB 40601 randomized trials.

The identification of prognostic markers in patients receiving neoadjuvant therapy is crucial for treatment optimization in HER2-positive breast cancer, with the immune microenvironment being a key factor. Here, we investigate the complexity of B and T cell receptor (BCR and TCR) repertoires in the context of two phase III trials, NeoALTTO and CALGB 40601, evaluating neoadjuvant paclitaxel with trastuzumab and/or lapatinib in women with HER2-positive breast cancer. BCR features, particularly the number of reads and clones, evenness and Gini index, are heterogeneous according to hormone receptor status and PAM50 subtypes.

Integrated Multimodal Analyses of DNA Damage Response and Immune Markers as Predictors of Response in Metastatic Triple-Negative Breast Cancer in the TNT Trial (NCT00532727).

Purpose: The TNT trial (NCT00532727) showed no evidence of carboplatin superiority over docetaxel in metastatic triple-negative breast cancer (mTNBC), but carboplatin benefit was observed in the germline BRCA1/2 mutation subgroup. Broader response-predictive biomarkers are needed. We explored the predictive ability of DNA damage response (DDR) and immune markers.

Incorporating RNA-based Risk Scores for Genomic Instability to Predict Breast Cancer Recurrence and Immunogenicity in a Diverse Population.

Unlabelled: Markers of genomic instability, including TP53 status and homologous recombination deficiency (HRD), are candidate biomarkers of immunogenicity and immune-mediated survival, but little is known about the distribution of these markers in large, population-based cohorts of racially diverse patients with breast cancer. In prior clinical trials, DNA-based approaches have been emphasized, but recent data suggest that RNA-based assessment can capture pathway differences conveniently and may be streamlined with other RNA-based genomic risk scores. Thus, we used RNA expression to study genomic instability (HRD and TP53 pathways) in context of the breast cancer immune microenvironment in three datasets (total = 4,892), including 1,942 samples from the Carolina Breast Cancer Study, a population-based study that oversampled Black ( = 1,026) and younger women ( = 1,032).

Landscape of Genetic Alterations Underlying Hallmark Signature Changes in Cancer Reveals Aneuploidy-driven Metabolic Reprogramming.

Unlabelled: The hallmark signatures based on gene expression capture core cancer processes. Through a pan-cancer analysis, we describe the overview of hallmark signatures across tumor types/subtypes and reveal significant relationships between these signatures and genetic alterations. mutation exerts diverse changes, including increased proliferation and glycolysis, which are closely mimicked by widespread copy-number alterations.

Diffsig: Associating Risk Factors With Mutational Signatures.

Somatic mutational signatures elucidate molecular vulnerabilities to therapy and therefore detecting signatures and classifying tumors with respect to signatures has clinical value. However, identifying the etiology of the mutational signatures remains a statistical challenge, with both small sample sizes and high variability in classification algorithms posing barriers. As a result, few signatures have been strongly linked to particular risk factors.

Prognostic and Predictive Value of Immune-Related Gene Expression Signatures vs Tumor-Infiltrating Lymphocytes in Early-Stage ERBB2/HER2-Positive Breast Cancer: A Correlative Analysis of the CALGB 40601 and PAMELA Trials.

Importance: Both tumor-infiltrating lymphocytes (TILs) assessment and immune-related gene expression signatures by RNA profiling predict higher pathologic complete response (pCR) and improved event-free survival (EFS) in patients with early-stage ERBB2/HER2-positive breast cancer. However, whether these 2 measures of immune activation provide similar or additive prognostic value is not known.

Objective: To examine the prognostic ability of TILs and immune-related gene expression signatures, alone and in combination, to predict pCR and EFS in patients with early-stage ERBB2/HER2-positive breast cancer treated in 2 clinical trials.

Multiomics in primary and metastatic breast tumors from the AURORA US network finds microenvironment and epigenetic drivers of metastasis.

The AURORA US Metastasis Project was established with the goal to identify molecular features associated with metastasis. We assayed 55 females with metastatic breast cancer (51 primary cancers and 102 metastases) by RNA sequencing, tumor/germline DNA exome and low-pass whole-genome sequencing and global DNA methylation microarrays. Expression subtype changes were observed in ~30% of samples and were coincident with DNA clonality shifts, especially involving HER2.

Collaborative study from the Bladder Cancer Advocacy Network for the genomic analysis of metastatic urothelial cancer.

Urothelial Cancer - Genomic Analysis to Improve Patient Outcomes and Research (NCT02643043), UC-GENOME, is a genomic analysis and biospecimen repository study in 218 patients with metastatic urothelial carcinoma. Here we report on the primary outcome of the UC-GENOME-the proportion of subjects who received next generation sequencing (NGS) with treatment options-and present the initial genomic analyses and clinical correlates. 69.