A multimodal atlas of hepatocellular carcinoma reveals convergent evolutionary paths and 'bad apple' effect on clinical trajectory.

BACKGROUND & AIMS: Hepatocellular carcinoma (HCC) is a highly fatal cancer characterized by high intra-tumor heterogeneity (ITH). A panoramic understanding of its tumor evolution, in relation to its clinical trajectory, may provide novel prognostic and treatment strategies. METHODS: Through the Asia-Pacific Hepatocellular Carcinoma trials group (NCT03267641), we recruited one of the largest prospective cohorts of patients with HCC, with over 600 whole genome and transcriptome samples from 123 treatment-naïve patients. RESULTS: Using a multi-region sampling approach, we revealed seven convergent genetic evolutionary paths governed by the early driver mutations, late copy number variations and viral integrations, which stratify patient clinical trajectories after surgical resection. Furthermore, such evolutionary paths shaped the molecular profiles, leading to distinct transcriptomic subtypes. Most significantly, although we found the coexistence of multiple transcriptomic subtypes within certain tumors, patient prognosis was best predicted by the most aggressive cell fraction of the tumor, rather than by overall degree of transcriptomic ITH level - a phenomenon we termed the 'bad apple' effect. Finally, we found that characteristics throughout early and late tumor evolution provide significant and complementary prognostic power in predicting patient survival. CONCLUSIONS: Taken together, our study generated a comprehensive landscape of evolutionary history for HCC and provides a rich multi-omics resource for understanding tumor heterogeneity and clinical trajectories. IMPACT AND IMPLICATIONS: This prospective study, utilizing comprehensive multi-sector, multi-omics sequencing and clinical data from surgically resected hepatocellular carcinoma (HCC), reveals critical insights into the role of tumor evolution and intra-tumor heterogeneity (ITH) in determining the prognosis of HCC. These findings are invaluable for oncology researchers and clinicians, as they underscore the influence of distinct evolutionary paths and the 'bad apple' effect, where the most aggressive tumor fraction dictates disease progression. These insights not only enhance prognostic accuracy post-surgical resection but also pave the way for personalized treatment strategies tailored to specific tumor evolutionary and transcriptomic profiles. The coexistence of multiple subtypes within the same tumor prompts a re-appraisal of the utilities of depending on single samples to represent the entire tumor and suggests the need for clinical molecular imaging. This research thus marks a significant step forward in the clinical understanding and management of HCC, underscoring the importance of integrating tumor evolutionary dynamics and multi-omics biomarkers into therapeutic decision-making. CLINICAL TRIAL NUMBER: NCT03267641 (Observational cohort).

PSiTE: a Phylogeny guided Simulator for Tumor Evolution.

SUMMARY: Simulating realistic clonal dynamics of tumors is an important topic in cancer genomics. Here, we present Phylogeny guided Simulator for Tumor Evolution, a tool that can simulate different types of tumor samples including single sector, multi-sector bulk tumor as well as single-cell tumor data under a wide range of evolutionary trajectories. Phylogeny guided Simulator for Tumor Evolution provides an efficient tool for understanding clonal evolution of cancer. AVAILABILITY AND IMPLEMENTATION: PSiTE is implemented in Python and is available at https://github.com/hchyang/PSiTE. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Passage Adaptation Correlates With the Reduced Efficacy of the Influenza Vaccine.

BACKGROUND: As a dominant seasonal influenza virus, H3N2 virus rapidly evolves in humans and is a constant threat to public health. Despite sustained research efforts, the efficacy of H3N2 vaccine has decreased rapidly. Even though antigenic drift and passage adaptation (substitutions accumulated during vaccine production in embryonated eggs) have been implicated in reduced vaccine efficacy (VE), their respective contributions to the phenomenon remain controversial. METHODS: We utilized mutational mapping, a powerful probabilistic method for studying sequence evolution, to analyze patterns of substitutions in different passage conditions for an unprecedented amount of H3N2 hemagglutinin sequences (n = 32 278). RESULTS: We found that passage adaptation in embryonated eggs is driven by repeated convergent evolution over 12 codons. Based on substitution patterns at these sites, we developed a metric, adaptive distance (AD), to quantify the strength of passage adaptation and subsequently identified a strong negative correlation between AD and VE. CONCLUSIONS: The high correlation between AD and VE implies that passage adaptation in embryonated eggs may be a strong contributor to the recent reduction in H3N2 VE. We developed a computational package called MADE (Measuring Adaptive Distance and vaccine Efficacy based on allelic barcodes) to measure the strength of passage adaptation and predict the efficacy of a candidate vaccine strain. Our findings shed light on strategies for reducing Darwinian evolution within the passaging medium in order to potentially restore an effective vaccine program in the future.

Examining Community-Based Housing Models to Support Aging in Place: A Scoping Review.

BACKGROUND AND OBJECTIVES: With the global population aging, there is a demand for older adults to age in place, that is, to live and age well in their home and community with some level of independence. Community-based housing models exist and may support this process. This scoping review aimed to describe and synthesize the ways in which community-based housing models relate to older adults' aging in place and identify strengths and gaps in the literature. RESEARCH DESIGN AND METHODS: The housing models explored were villages, naturally occurring retirement communities, congregate housing and cohousing, sheltered housing, and continuing care retirement communities. This exploratory scoping review examined international peer-reviewed literature published from 2004 to 2019. Six databases were searched using terms related to housing models and older adults. Forty-six articles met the inclusion criteria. Descriptive numerical summary and thematic analysis were used to synthesize study characteristics and findings. RESULTS: Our analysis revealed 4 themes relating to aging in place in the housing models: Social Relations, Health and Well-being, Sense of Self and Autonomy, and Activity Participation. Further analysis identified housing-specific characteristics that appeared to pose barriers to, or enable, aging in place. DISCUSSION AND IMPLICATIONS: To best support aging in place, the findings of the review suggest multiple characteristics worth considering when developing or relocating to a community-based housing model. Further research is required to understand how facilitating characteristics can promote aging in place for community-dwelling older adults.

Genome instability is associated with ethnic differences between Asians and Europeans in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is one of the deadliest cancer types with diverse etiological factors across the world. Although large scale genomic studies have been conducted in different countries, integrative analysis of HCC genomes and ethnic comparison across cohorts are lacking. Methods: We first integrated genomes of 1,349 HCC patients from five large cohorts across the world and applied multiple statistical methods in identifying driver genes. Subsequently, we systematically compared HCC genomes and transcriptomes between Asians and Europeans using the TCGA cohort. Results: We identified 29 novel candidate driver genes, many of which are infrequent tumor suppressors driving late-stage tumor progression. When we systematically compared ethnic differences in the genomic landscape between Asian and European HCCs using the TCGA cohort (n = 348), we found little differences in driver frequencies. Through multi-modal integrative analysis, we found higher genomic instability in Asians together with a collection of molecular events ranging from tumor mutation burden (TMB), copy number alterations as well as transcriptomic subtypes segregating distinctively between two ethnic backgrounds. Strikingly, we identified an Asian specific transcriptomic subtype with multiple ethnically enriched genomic alterations, in particular chromosome 16 deletion, leading to a clinically aggressive RNA subgroup unique to Asians. Integrating multi-modal information, we found that survival models predict patient prognosis much better in Asians than in Europeans, demonstrating a higher potential for precision medicine applications in Asia. Conclusion: For the first time, we have uncovered an unprecedented amount of genomic differences segregating distinctively across ethnicities in HCC and highlighted the importance of differential disease biology and management in HCC across ethnic backgrounds.

Obtaining spatially resolved tumor purity maps using deep multiple instance learning in a pan-cancer study.

Tumor purity is the percentage of cancer cells within a tissue section. Pathologists estimate tumor purity to select samples for genomic analysis by manually reading hematoxylin-eosin (H&E)-stained slides, which is tedious, time consuming, and prone to inter-observer variability. Besides, pathologists' estimates do not correlate well with genomic tumor purity values, which are inferred from genomic data and accepted as accurate for downstream analysis. We developed a deep multiple instance learning model predicting tumor purity from H&E-stained digital histopathology slides. Our model successfully predicted tumor purity in eight The Cancer Genome Atlas (TCGA) cohorts and a local Singapore cohort. The predictions were highly consistent with genomic tumor purity values. Thus, our model can be utilized to select samples for genomic analysis, which will help reduce pathologists' workload and decrease inter-observer variability. Furthermore, our model provided tumor purity maps showing the spatial variation within sections. They can help better understand the tumor microenvironment.

A Randomized Phase 2 Trial of Nivolumab Versus Nivolumab-Ipilimumab Combination in EGFR-Mutant NSCLC.

Introduction: Although immune checkpoint inhibitors (ICIs) have dramatically improved outcomes for nononcogene-addicted NSCLC, monotherapy with programmed cell death protein-1 (PD1) inhibition has been associated with low efficacy in the EGFR-mutant setting. Given the potential for synergism with combination checkpoint blockade, we designed a trial to test the activity of combination nivolumab (N)-ipilimumab (NI) in EGFR-mutant NSCLC. Methods: This is a randomized phase 2 study (NCT03091491) of N versus NI combination in EGFR tyrosine kinase inhibitor (TKI)-resistant NSCLC, with crossover permitted on disease progression. The primary end point was the objective response rate, and the secondary end points included progression-free survival, overall survival, and safety of ICI after EGFR TKI. Results: Recruitment ceased owing to futility after 31 of 184 planned patients were treated. A total of 15 patients received N and 16 received NI combination. There were 16 patients (51.6%) who had programmed death-ligand (PDL1) 1 greater than or equal to 1%, and 15 (45.2%) harbored EGFR T790M. Five patients derived clinical benefits from ICI with one objective response (objective response rate 3.2%), and median progression-free survival was 1.22 months (95% confidence interval: 1.15-1.35) for the overall cohort. None of the four patients who crossed over achieved salvage response by NI. PDL1 and tumor mutational burden (TMB) were not able to predict ICI response. Rates of all grade immune-related adverse events were similar (80% versus 75%), with only two grade 3 events. Conclusions: Immune checkpoint inhibition is ineffective in EGFR TKI-resistant NSCLC. Whereas a small subgroup of EGFR-mutant NSCLC may be immunogenic and responsive to ICI, better biomarkers are needed to select appropriate patients.

Dynamic phenotypic heterogeneity and the evolution of multiple RNA subtypes in hepatocellular carcinoma: the PLANET study.

Intra-tumor heterogeneity (ITH) is a key challenge in cancer treatment, but previous studies have focused mainly on the genomic alterations without exploring phenotypic (transcriptomic and immune) heterogeneity. Using one of the largest prospective surgical cohorts for hepatocellular carcinoma (HCC) with multi-region sampling, we sequenced whole genomes and paired transcriptomes from 67 HCC patients (331 samples). We found that while genomic ITH was rather constant across stages, phenotypic ITH had a very different trajectory and quickly diversified in stage II patients. Most strikingly, 30% of patients were found to contain more than one transcriptomic subtype within a single tumor. Such phenotypic ITH was found to be much more informative in predicting patient survival than genomic ITH and explains the poor efficacy of single-target systemic therapies in HCC. Taken together, we not only revealed an unprecedentedly dynamic landscape of phenotypic heterogeneity in HCC, but also highlighted the importance of studying phenotypic evolution across cancer types.

Integrative Profiling of T790M-Negative EGFR-Mutated NSCLC Reveals Pervasive Lineage Transition and Therapeutic Opportunities.

PURPOSE: Despite the established role of EGFR tyrosine kinase inhibitors (TKIs) in EGFR-mutated NSCLC, drug resistance inevitably ensues, with a paucity of treatment options especially in EGFR T790M-negative resistance. EXPERIMENTAL DESIGN: We performed whole-exome and transcriptome analysis of 59 patients with first- and second-generation EGFR TKI-resistant metastatic EGFR-mutated NSCLC to characterize and compare molecular alterations mediating resistance in T790M-positive (T790M+) and -negative (T790M-) disease. RESULTS: Transcriptomic analysis revealed ubiquitous loss of adenocarcinoma lineage gene expression in T790M- tumors, orthogonally validated using multiplex IHC. There was enrichment of genomic features such as TP53 alterations, 3q chromosomal amplifications, whole-genome doubling and nonaging mutational signatures in T790M- tumors. Almost half of resistant tumors were further classified as immunehot, with clinical outcomes conditional on immune cell-infiltration state and T790M status. Finally, using a Bayesian statistical approach, we explored how T790M- and T790M+ disease might be predicted using comprehensive genomic and transcriptomic profiles of treatment-naïve patients. CONCLUSIONS: Our results illustrate the interplay between genetic alterations, cell lineage plasticity, and immune microenvironment in shaping divergent TKI resistance and outcome trajectories in EGFR-mutated NSCLC. Genomic and transcriptomic profiling may facilitate the design of bespoke therapeutic approaches tailored to a tumor's adaptive potential.

Genomic landscape of lung adenocarcinoma in East Asians.

Lung cancer is the world's leading cause of cancer death and shows strong ancestry disparities. By sequencing and assembling a large genomic and transcriptomic dataset of lung adenocarcinoma (LUAD) in individuals of East Asian ancestry (EAS; n = 305), we found that East Asian LUADs had more stable genomes characterized by fewer mutations and fewer copy number alterations than LUADs from individuals of European ancestry. This difference is much stronger in smokers as compared to nonsmokers. Transcriptomic clustering identified a new EAS-specific LUAD subgroup with a less complex genomic profile and upregulated immune-related genes, allowing the possibility of immunotherapy-based approaches. Integrative analysis across clinical and molecular features showed the importance of molecular phenotypes in patient prognostic stratification. EAS LUADs had better prediction accuracy than those of European ancestry, potentially due to their less complex genomic architecture. This study elucidated a comprehensive genomic landscape of EAS LUADs and highlighted important ancestry differences between the two cohorts.