Venn and Euler diagrams are a popular yet inadequate solution for quantitative visualization of set intersections. A scalable alternative to Venn and Euler diagrams for visualizing intersecting sets ...and their properties is needed.
We developed UpSetR, an open source R package that employs a scalable matrix-based visualization to show intersections of sets, their size, and other properties.
UpSetR is available at https://github.com/hms-dbmi/UpSetR/ and released under the MIT License. A Shiny app is available at https://gehlenborglab.shinyapps.io/upsetr/ .
nils@hms.harvard.edu.
Supplementary data are available at Bioinformatics online.
Immune checkpoint blockade (ICB) therapies, which potentiate the body's natural immune response against tumor cells, have shown immense promise in the treatment of various cancers. Currently, tumor ...mutational burden (TMB) and programmed death ligand 1 (PD-L1) expression are the primary biomarkers evaluated for clinical management of cancer patients across histologies. However, the wide range of responses has demonstrated that the specific molecular and genetic characteristics of each patient's tumor and immune system must be considered to maximize treatment efficacy. Here, we review the various biological pathways and emerging biomarkers implicated in response to PD-(L)1 and cytotoxic T lymphocyte-associated antigen 4 (CTLA-4) therapies, including oncogenic signaling pathways, human leukocyte antigen (HLA) variability, mutation and neoantigen burden, microbiome composition, endogenous retroviruses (ERV), and deficiencies in chromatin remodeling and DNA damage repair (DDR) machinery. We also discuss several mechanisms that have been observed to confer resistance to ICB, such as loss of phosphatase and tensin homolog (PTEN), loss of major histocompatibility complex (MHC) I/II expression, and activation of the indoleamine 2,3-dioxygenase 1 (IDO1) and transforming growth factor beta (TGFβ) pathways. Clinical trials testing the combination of PD-(L)1 or CTLA-4 blockade with molecular mediators of these pathways are becoming more common and may hold promise for improving treatment efficacy and response. Ultimately, some of the genes and molecular mechanisms highlighted in this review may serve as novel biological targets or therapeutic vulnerabilities to improve clinical outcomes in patients.
We performed harmonized molecular and clinical analysis on 1,048 melanomas and discovered markedly different global genomic properties among subtypes (BRAF, (N)RAS, NF1, triple wild-type (TWT)), ...subtype-specific preferences for secondary driver genes and active mutational processes previously unreported in melanoma. Secondary driver genes significantly enriched in specific subtypes reflected preferential dysregulation of additional pathways, such as induction of transforming growth factor-β signaling in BRAF melanomas and inactivation of the SWI/SNF complex in (N)RAS melanomas, and select co-mutation patterns coordinated selective response to immune checkpoint blockade. We also defined the mutational landscape of TWT melanomas and revealed enrichment of DNA-repair-defect signatures in this subtype, which were associated with transcriptional downregulation of key DNA-repair genes, and may revive previously discarded or currently unconsidered therapeutic modalities for genomically stratified melanoma patient subsets. Broadly, harmonized meta-analysis of melanoma whole exomes revealed distinct molecular drivers that may point to multiple opportunities for biological and therapeutic investigation.
Less than 10% of patients with cancer have detectable pathogenic germline alterations, which may be partially due to incomplete pathogenic variant detection.
To evaluate if deep learning approaches ...identify more germline pathogenic variants in patients with cancer.
A cross-sectional study of a standard germline detection method and a deep learning method in 2 convenience cohorts with prostate cancer and melanoma enrolled in the US and Europe between 2010 and 2017. The final date of clinical data collection was December 2017.
Germline variant detection using standard or deep learning methods.
The primary outcomes included pathogenic variant detection performance in 118 cancer-predisposition genes estimated as sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). The secondary outcomes were pathogenic variant detection performance in 59 genes deemed actionable by the American College of Medical Genetics and Genomics (ACMG) and 5197 clinically relevant mendelian genes. True sensitivity and true specificity could not be calculated due to lack of a criterion reference standard, but were estimated as the proportion of true-positive variants and true-negative variants, respectively, identified by each method in a reference variant set that consisted of all variants judged to be valid from either approach.
The prostate cancer cohort included 1072 men (mean SD age at diagnosis, 63.7 7.9 years; 857 79.9% with European ancestry) and the melanoma cohort included 1295 patients (mean SD age at diagnosis, 59.8 15.6 years; 488 37.7% women; 1060 81.9% with European ancestry). The deep learning method identified more patients with pathogenic variants in cancer-predisposition genes than the standard method (prostate cancer: 198 vs 182; melanoma: 93 vs 74); sensitivity (prostate cancer: 94.7% vs 87.1% difference, 7.6%; 95% CI, 2.2% to 13.1%; melanoma: 74.4% vs 59.2% difference, 15.2%; 95% CI, 3.7% to 26.7%), specificity (prostate cancer: 64.0% vs 36.0% difference, 28.0%; 95% CI, 1.4% to 54.6%; melanoma: 63.4% vs 36.6% difference, 26.8%; 95% CI, 17.6% to 35.9%), PPV (prostate cancer: 95.7% vs 91.9% difference, 3.8%; 95% CI, -1.0% to 8.4%; melanoma: 54.4% vs 35.4% difference, 19.0%; 95% CI, 9.1% to 28.9%), and NPV (prostate cancer: 59.3% vs 25.0% difference, 34.3%; 95% CI, 10.9% to 57.6%; melanoma: 80.8% vs 60.5% difference, 20.3%; 95% CI, 10.0% to 30.7%). For the ACMG genes, the sensitivity of the 2 methods was not significantly different in the prostate cancer cohort (94.9% vs 90.6% difference, 4.3%; 95% CI, -2.3% to 10.9%), but the deep learning method had a higher sensitivity in the melanoma cohort (71.6% vs 53.7% difference, 17.9%; 95% CI, 1.82% to 34.0%). The deep learning method had higher sensitivity in the mendelian genes (prostate cancer: 99.7% vs 95.1% difference, 4.6%; 95% CI, 3.0% to 6.3%; melanoma: 91.7% vs 86.2% difference, 5.5%; 95% CI, 2.2% to 8.8%).
Among a convenience sample of 2 independent cohorts of patients with prostate cancer and melanoma, germline genetic testing using deep learning, compared with the current standard genetic testing method, was associated with higher sensitivity and specificity for detection of pathogenic variants. Further research is needed to understand the relevance of these findings with regard to clinical outcomes.
Radiation-associated muscle-invasive bladder cancers arising after prostate cancer radiotherapy have mutational evidence of radiation exposure but share many driver alterations with ...non–radiation-associated muscle-invasive bladder cancers.
Muscle-invasive bladder cancer (MIBC) is a rare but serious event following definitive radiation for prostate cancer. Radiation-associated MIBC (RA-MIBC) can be difficult to manage given the challenges of delivering definitive therapy to a previously irradiated pelvis. The genomic landscape of RA-MIBC and whether it is distinct from non–RA-MIBC are unknown.
To define mutational features of RA-MIBC and compare the genomic landscape of RA-MIBC with that of non–RA-MIBC.
We identified patients from our institution who received radiotherapy for prostate cancer and subsequently developed MIBC.
We performed whole exome sequencing of bladder tumors from RA-MIBC patients. Tumor genetic alterations including mutations, copy number alterations, and mutational signatures were identified and were compared with genetic features of non–RA-MIBC. We used the Kaplan-Meier method to estimate recurrence-free (RFS) and overall (OS) survival.
We identified 19 RA-MIBC patients with available tumor tissue (n = 22 tumors) and clinical data. The median age was 76 yr, and the median time from prostate cancer radiation to RA-MIBC was 12 yr. The median RFS was 14.5 mo and the median OS was 22.0 mo. Compared with a cohort of non–RA-MIBC analyzed in parallel, there was no difference in tumor mutational burden, but RA-MIBCs had a significantly increased number of short insertions and deletions (indels) consistent with previous radiation exposure. We identified mutation signatures characteristic of APOBEC-mediated mutagenesis, aging, and homologous recombination deficiency. The frequency of mutations in many known bladder cancer genes, including TP53, KDM6A, and RB1, as well as copy number alterations such as CDKN2A loss was similar in RA-MIBC and non-RA-MIBC.
We identified unique mutational properties that likely contribute to the distinct biological and clinical features of RA-MIBC.
Bladder cancer is a rare but serious diagnosis following radiation for prostate cancer. We characterized genetic features of bladder tumors arising after prostate radiotherapy, and identify similarities with and differences from bladder tumors from patients without previous radiation.
High-risk localized prostate cancer (HRLPC) is associated with a substantial risk of recurrence and disease mortality. Recent clinical trials have shown that intensifying anti-androgen therapies ...administered before prostatectomy can induce pathologic complete responses or minimal residual disease, called exceptional response, although the molecular determinants of these clinical outcomes are largely unknown. Here, we perform whole-exome and transcriptome sequencing on pre-treatment multi-regional tumor biopsies from exceptional responders (ERs) and non-responders (NRs, pathologic T3 or lymph node-positive disease) to intensive neoadjuvant anti-androgen therapies. Clonal SPOP mutation and SPOPL copy-number loss are exclusively observed in ERs, while clonal TP53 mutation and PTEN copy-number loss are exclusively observed in NRs. Transcriptional programs involving androgen signaling and TGF-β signaling are enriched in ERs and NRs, respectively. These findings may guide prospective validation studies of these molecular features in large HRLPC clinical cohorts treated with neoadjuvant anti-androgens to improve patient stratification.
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•SPOP mutation and SPOPL copy-number loss are exclusive to exceptional responders•Androgen signaling expression programs are enriched in exceptional responders•Non-responders harbor TP53 mutation, PTEN loss, and TGF-β transcriptional programs•Phylogenetic analysis reveals that key response mediators are truncal in nature
Leveraging pretreatment multi-regional biopsies from patients with high-risk localized prostate cancer, Tewari et al. identify whole-exome and transcriptional features associated with exceptional response and non-response to neoadjuvant androgen pathway inhibition. These results may have major stratification and treatment implications for this large patient population.
Gene fusions frequently result from rearrangements in cancer genomes. In many instances, gene fusions play an important role in oncogenesis; in other instances, they are thought to be passenger ...events. Although regulatory element rearrangements and copy number alterations resulting from these structural variants are known to lead to transcriptional dysregulation across cancers, the extent to which these events result in functional dependencies with an impact on cancer cell survival is variable. Here we used CRISPR-Cas9 dependency screens to evaluate the fitness impact of 3,277 fusions across 645 cell lines from the Cancer Dependency Map. We found that 35% of cell lines harbored either a fusion partner dependency or a collateral dependency on a gene within the same topologically associating domain as a fusion partner. Fusion-associated dependencies revealed numerous novel oncogenic drivers and clinically translatable alterations. Broadly, fusions can result in partner and collateral dependencies that have biological and clinical relevance across cancer types. SIGNIFICANCE: This study provides insights into how fusions contribute to fitness in different cancer contexts beyond partner-gene activation events, identifying partner and collateral dependencies that may have direct implications for clinical care.