Impact of depatuxizumab mafodotin on health-related quality of life and neurological functioning in the phase II EORTC 1410/INTELLANCE 2 trial for EGFR-amplified recurrent glioblastoma

Background: In the EORTC 1410/ INTELLANCE 2 randomised, phase II study (NCT02343406), with the antibody-edrug conjugate depatuxizumab mafodotin (Depatux-M, ABT-414) in patients with recurrent EGFR-amplified glioblastoma, the primary end-point (overall survival) was not met, and the drug had ocular dose-limiting toxicity. This study reports results from the prespecified health-related quality of life (HRQoL) and neurological deterioration-free survival (NDFS) exploratory analysis.Patients and methods: Patients (n Z 260) were randomised 1:1:1 to receive either Depatux-M 1.25 mg/kg or 1.0 mg/kg intravenously every 2 weeks with oral temozolomide (TMZ) 150 mg/ m(2), Depatux-M alone, or TMZ or oral lomustine (CCNU) 110 mg/ m(2) ( TMZ/CCNU). HRQoL outcomes were recorded using the EORTC core Quality of Life QLQ-C30, and brain cancer-specific QLQ-BN20 questionnaires. Questionnaires were completed at baseline, weeks 8 and 16, and month 6, and changes from baseline to each time point were calculated. NDFS was defined as time to first deterioration in World Health Organisation performance status.Results: Compliance with HRQoL was 88.1% at baseline and decreased to 37.9% at month 6. Differences from baseline between Depatux-M arms and TMZ/CCNU in global health/QoL status throughout treatment did not reach clinical relevance (>= 10 points). Self-reported visual disorders deteriorated to a clinically relevant extent with Depatux-M arms versus TMZ/CCNU at all timepoints (mean differences range: 24.6-35.1 points). Changes from baseline for other HRQoL scales and NDFS were generally similar between treatment arms.Conclusions: Depatux-M had no impact on HRQoL and NDFS in patients with EGFRamplified recurrent glioblastoma, except for more visual disorders, an expected side- effect of the study drug. (C) 2021 Elsevier Ltd. All rights reserved.Neurolog

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

Measurement of CP observables in B± → D(⁎)K± and B± → D(⁎)π± decays

Measurements of CP observables in B ± →D (⁎) K ± and B ± →D (⁎) π ± decays are presented, where D (⁎) indicates a neutral D or D ⁎ meson that is an admixture of D (⁎)0 and D¯ (⁎)0 states. Decays of the D ⁎ meson to the Dπ 0 and Dγ final states are partially reconstructed without inclusion of the neutral pion or photon, resulting in distinctive shapes in the B candidate invariant mass distribution. Decays of the D meson are fully reconstructed in the K ± π ∓ , K + K − and π + π − final states. The analysis uses a sample of charged B mesons produced in pp collisions collected by the LHCb experiment, corresponding to an integrated luminosity of 2.0, 1.0 and 2.0 fb −1 taken at centre-of-mass energies of s=7, 8 and 13 TeV, respectively. The study of B ± →D ⁎ K ± and B ± →D ⁎ π ± decays using a partial reconstruction method is the first of its kind, while the measurement of B ± →DK ± and B ± →Dπ ± decays is an update of previous LHCb measurements. The B ± →DK ± results are the most precise to date

Driver Fusions and Their Implications in the Development and Treatment of Human Cancers.

Gene fusions represent an important class of somatic alterations in cancer. We systematically investigated fusions in 9,624 tumors across 33 cancer types using multiple fusion calling tools. We identified a total of 25,664 fusions, with a 63% validation rate. Integration of gene expression, copy number, and fusion annotation data revealed that fusions involving oncogenes tend to exhibit increased expression, whereas fusions involving tumor suppressors have the opposite effect. For fusions involving kinases, we found 1,275 with an intact kinase domain, the proportion of which varied significantly across cancer types. Our study suggests that fusions drive the development of 16.5% of cancer cases and function as the sole driver in more than 1% of them. Finally, we identified druggable fusions involving genes such as TMPRSS2, RET, FGFR3, ALK, and ESR1 in 6.0% of cases, and we predicted immunogenic peptides, suggesting that fusions may provide leads for targeted drug and immune therapy

Machine Learning Detects Pan-cancer Ras Pathway Activation in The Cancer Genome Atlas

Precision oncology uses genomic evidence to match patients with treatment but often fails to identify all patients who may respond. The transcriptome of these \u201chidden responders\u201d may reveal responsive molecular states. We describe and evaluate a machine-learning approach to classify aberrant pathway activity in tumors, which may aid in hidden responder identification. The algorithm integrates RNA-seq, copy number, and mutations from 33 different cancer types across The Cancer Genome Atlas (TCGA) PanCanAtlas project to predict aberrant molecular states in tumors. Applied to the Ras pathway, the method detects Ras activation across cancer types and identifies phenocopying variants. The model, trained on human tumors, can predict response to MEK inhibitors in wild-type Ras cell lines. We also present data that suggest that multiple hits in the Ras pathway confer increased Ras activity. The transcriptome is underused in precision oncology and, combined with machine learning, can aid in the identification of hidden responders. Way et al. develop a machine-learning approach using PanCanAtlas data to detect Ras activation in cancer. Integrating mutation, copy number, and expression data, the authors show that their method detects Ras-activating variants in tumors and sensitivity to MEK inhibitors in cell lines

Cell-of-Origin Patterns Dominate the Molecular Classification of 10,000 Tumors from 33 Types of Cancer

We conducted comprehensive integrative molecular analyses of the complete set of tumors in The Cancer Genome Atlas (TCGA), consisting of approximately 10,000 specimens and representing 33 types of cancer. We performed molecular clustering using data on chromosome-arm-level aneuploidy, DNA hypermethylation, mRNA, and miRNA expression levels and reverse-phase protein arrays, of which all, except for aneuploidy, revealed clustering primarily organized by histology, tissue type, or anatomic origin. The influence of cell type was evident in DNA-methylation-based clustering, even after excluding sites with known preexisting tissue-type-specific methylation. Integrative clustering further emphasized the dominant role of cell-of-origin patterns. Molecular similarities among histologically or anatomically related cancer types provide a basis for focused pan-cancer analyses, such as pan-gastrointestinal, pan-gynecological, pan-kidney, and pan-squamous cancers, and those related by stemness features, which in turn may inform strategies for future therapeutic development. Comprehensive, integrated molecular analysis identifies molecular relationships across a large diverse set of human cancers, suggesting future directions for exploring clinical actionability in cancer treatment

Perspective on Oncogenic Processes at the End of the Beginning of Cancer Genomics

The Cancer Genome Atlas (TCGA) has catalyzed systematic characterization of diverse genomic alterations underlying human cancers. At this historic junction marking the completion of genomic characterization of over 11,000 tumors from 33 cancer types, we present our current understanding of the molecular processes governing oncogenesis. We illustrate our insights into cancer through synthesis of the findings of the TCGA PanCancer Atlas project on three facets of oncogenesis: (1) somatic driver mutations, germline pathogenic variants, and their interactions in the tumor; (2) the influence of the tumor genome and epigenome on transcriptome and proteome; and (3) the relationship between tumor and the microenvironment, including implications for drugs targeting driver events and immunotherapies. These results will anchor future characterization of rare and common tumor types, primary and relapsed tumors, and cancers across ancestry groups and will guide the deployment of clinical genomic sequencing

Comparative Molecular Analysis of Gastrointestinal Adenocarcinomas

We analyzed 921 adenocarcinomas of the esophagus, stomach, colon, and rectum to examine shared and distinguishing molecular characteristics of gastrointestinal tract adenocarcinomas (GIACs). Hypermutated tumors were distinct regardless of cancer type and comprised those enriched for insertions/deletions, representing microsatellite instability cases with epigenetic silencing of MLH1 in the context of CpG island methylator phenotype, plus tumors with elevated single-nucleotide variants associated with mutations in POLE. Tumors with chromosomal instability were diverse, with gastroesophageal adenocarcinomas harboring fragmented genomes associated with genomic doubling and distinct mutational signatures. We identified a group of tumors in the colon and rectum lacking hypermutation and aneuploidy termed genome stable and enriched in DNA hypermethylation and mutations in KRAS, SOX9, and PCBP1. Liu et al. analyze 921 gastrointestinal (GI) tract adenocarcinomas and find that hypermutated tumors are enriched for insertions/deletions, upper GI tumors with chromosomal instability harbor fragmented genomes, and a group of genome-stable colorectal tumors are enriched in mutations in SOX9 and PCBP1

Oncogenic Signaling Pathways in The Cancer Genome Atlas

Genetic alterations in signaling pathways that control cell-cycle progression, apoptosis, and cell growth are common hallmarks of cancer, but the extent, mechanisms, and co-occurrence of alterations in these pathways differ between individual tumors and tumor types. Using mutations, copy-number changes, mRNA expression, gene fusions and DNA methylation in 9,125 tumors profiled by The Cancer Genome Atlas (TCGA), we analyzed the mechanisms and patterns of somatic alterations in ten canonical pathways: cell cycle, Hippo, Myc, Notch, Nrf2, PI-3-Kinase/Akt, RTK-RAS, TGFb signaling, p53 and beta-catenin/Wnt. We charted the detailed landscape of pathway alterations in 33 cancer types, stratified into 64 subtypes, and identified patterns of co-occurrence and mutual exclusivity. Eighty-nine percent of tumors had at least one driver alteration in these one alteration potentially targetable by currently available drugs. Thirty percent of tumors had multiple targetable alterations, indicating opportunities for combination therapy

Somatic Mutational Landscape of Splicing Factor Genes and Their Functional Consequences across 33 Cancer Types

Hotspot mutations in splicing factor genes have been recently reported at high frequency in hematological malignancies, suggesting the importance of RNA splicing in cancer. We analyzed whole-exome sequencing data across 33 tumor types in The Cancer Genome Atlas (TCGA), and we identified 119 splicing factor genes with significant non-silent mutation patterns, including mutation over-representation, recurrent loss of function (tumor suppressor-like), or hotspot mutation profile (oncogene-like). Furthermore, RNA sequencing analysis revealed altered splicing events associated with selected splicing factor mutations. In addition, we were able to identify common gene pathway profiles associated with the presence of these mutations. Our analysis suggests that somatic alteration of genes involved in the RNA-splicing process is common in cancer and may represent an underappreciated hallmark of tumorigenesis