Showing papers by "Samuel Aparicio published in 2020"

Pan-cancer analysis of whole genomes

Abstract: Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale1,2,3. Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4–5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter4; identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation5,6; analyses timings and patterns of tumour evolution7; describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity8,9; and evaluates a range of more-specialized features of cancer genomes8,10,11,12,13,14,15,16,17,18.

Eleven grand challenges in single-cell data science

Abstract: The recent boom in microfluidics and combinatorial indexing strategies, combined with low sequencing costs, has empowered single-cell sequencing technology. Thousands-or even millions-of cells analyzed in a single experiment amount to a data revolution in single-cell biology and pose unique data science problems. Here, we outline eleven challenges that will be central to bringing this emerging field of single-cell data science forward. For each challenge, we highlight motivating research questions, review prior work, and formulate open problems. This compendium is for established researchers, newcomers, and students alike, highlighting interesting and rewarding problems for the coming years.

Imaging mass cytometry and multiplatform genomics define the phenogenomic landscape of breast cancer

Abstract: Genomic alterations shape cell phenotypes and the structure of tumor ecosystems in poorly defined ways. To investigate these relationships, we used imaging mass cytometry to quantify the expression of 37 proteins with subcellular spatial resolution in 483 tumors from the METABRIC cohort. Single-cell analysis revealed cell phenotypes spanning epithelial, stromal and immune types. Distinct combinations of cell phenotypes and cell–cell interactions were associated with genomic subtypes of breast cancer. Epithelial luminal cell phenotypes separated into those predominantly impacted by mutations and those affected by copy number aberrations. Several features of tumor ecosystems, including cellular neighborhoods, were linked to prognosis, illustrating their clinical relevance. In summary, systematic analysis of single-cell phenotypic and spatial correlates of genomic alterations in cancer revealed how genomes shape both the composition and architecture of breast tumor ecosystems and will enable greater understanding of the phenotypic impact of genomic alterations. Bodenmiller and colleagues pair imaging mass cytometry with data from the METABRIC cohort to define single-cell phenotypic and genomic features of breast cancer with spatial context, finding associations with breast cancer subtypes and prognosis.

Chemogenomic profiling of breast cancer patient-derived xenografts reveals targetable vulnerabilities for difficult-to-treat tumors.

Abstract: Subsets of breast tumors present major clinical challenges, including triple-negative, metastatic/recurrent disease and rare histologies. Here, we developed 37 patient-derived xenografts (PDX) from these difficult-to-treat cancers to interrogate their molecular composition and functional biology. Whole-genome and transcriptome sequencing and reverse-phase protein arrays revealed that PDXs conserve the molecular landscape of their corresponding patient tumors. Metastatic potential varied between PDXs, where low-penetrance lung micrometastases were most common, though a subset of models displayed high rates of dissemination in organotropic or diffuse patterns consistent with what was observed clinically. Chemosensitivity profiling was performed in vivo with standard-of-care agents, where multi-drug chemoresistance was retained upon xenotransplantation. Consolidating chemogenomic data identified actionable features in the majority of PDXs, and marked regressions were observed in a subset that was evaluated in vivo. Together, this clinically-annotated PDX library with comprehensive molecular and phenotypic profiling serves as a resource for preclinical studies on difficult-to-treat breast tumors. Savage et al. employ whole-genome, transcriptome sequencing and reverse-phase protein arrays on 37 developed breast cancer patient-derived xenografts (PDX) models to identify potential therapeutic targets, metastatic potential and chemosensitivity. This PDX library with comprehensive molecular and phenotypic profiling could serve as a resource for preclinical studies on difficult-to-treat breast tumors.

Efficient Bayesian inference of phylogenetic trees from large scale, low-depth genome-wide single-cell data

Abstract: A new generation of scalable single cell whole genome sequencing (scWGS) methods, allows unprecedented high resolution measurement of the evolutionary dynamics of cancer cells populations. Phylogenetic reconstruction is central to identifying sub-populations and distinguishing mutational processes. The ability to sequence tens of thousands of single genomes at high resolution per experiment is challenging the assumptions and scalability of existing phylogenetic tree building methods and calls for tailored phylogenetic models and scalable inference algorithms. We propose a phylogenetic model and associated Bayesian inference procedure which exploits the specifics of scWGS data. A first highlight of our approach is a novel phylogenetic encoding of copy-number data providing an attractive statistical-computational trade-off by simplifying the site dependencies induced by rearrangements while still forming a sound foundation to phylogenetic inference. A second highlight is an innovative phylogenetic tree exploration move which makes the cost of MCMC iterations bounded by O(|C| + |L|), where |C| is the number of cells and |L| is the number of loci. In contrast, existing off-the-shelf likelihood-based methods incur iteration cost of O(|C| |L|). Moreover, the novel move considers an exponential number of neighbouring trees whereas off-the-shelf moves consider a polynomial size set of neighbours. The third highlight is a novel mutation calling method that incorporates the copy-number data and the underlying phylogenetic tree to overcome the missing data issue. This framework allows us to realistically consider routine Bayesian phylogenetic inference at the scale of scWGS data.

Epiclomal: Probabilistic clustering of sparse single-cell DNA methylation data.

Abstract: We present Epiclomal, a probabilistic clustering method arising from a hierarchical mixture model to simultaneously cluster sparse single-cell DNA methylation data and impute missing values. Using synthetic and published single-cell CpG datasets, we show that Epiclomal outperforms non-probabilistic methods and can handle the inherent missing data characteristic that dominates single-cell CpG genome sequences. Using newly generated single-cell 5mCpG sequencing data, we show that Epiclomal discovers sub-clonal methylation patterns in aneuploid tumour genomes, thus defining epiclones that can match or transcend copy number-determined clonal lineages and opening up an important form of clonal analysis in cancer. Epiclomal is written in R and Python and is available at https://github.com/shahcompbio/Epiclomal.

Age-correlated protein and transcript expression in breast cancer and normal breast tissues is dominated by host endocrine effects

Abstract: The magnitude and scope of intrinsic age-correlated and host endocrine age-correlated gene expression in breast cancer is not well understood. From age-correlated gene expression in 3,071 breast cancer transcriptomes and epithelial protein expression of 42 markers in 5,001 breast cancers and 537 normal breast tissues, we identified a majority of age-correlated genes as putatively regulated by age-dependent estrogen signaling. Surprisingly, these included genes encoding the chromatin modifier EZH2 (which had a negative age correlation) and associated H3K27me3 (which had an inverse, positive age correlation). Among The Cancer Genome Atlas lung, thyroid, kidney and prostate transcriptomes, the largest overlap with breast cancer in age-correlated transcripts was lung cancer, for which about one-third of overlapping age-correlated transcripts appeared to be estrogen regulated. Age-quartile-stratified outcomes analysis of 3,500 breast cancers using EZH2, H3K27me3, FOXA1 and BCL2 proteins revealed distinct age-related prognostic significance. Age correlation in gene expression may thus be an important factor in ER, EZH2, H3K27me3 and other biomarker assessment and treatment strategies. Aparicio and colleagues identify gene expression changes in breast cancer datasets putatively associated with age-related endocrine effects, suggesting that patient age may influence the prognostic potential of certain biomarkers.

Epiclomal: probabilistic clustering of sparse single-cell DNA methylation data

Abstract: We present Epiclomal, a probabilistic clustering method arising from a hierarchical mixture model to simultaneously cluster sparse single-cell DNA methylation data and impute missing values. Using synthetic and published single-cell CpG datasets we show that Epiclomal outperforms non-probabilistic methods and is able to handle the inherent missing data feature which dominates single-cell CpG genome sequences. Using a recently published single-cell 5mCpG sequencing method (PBAL), we show that Epiclomal discovers sub-clonal patterns of methylation in aneuploid tumour genomes, thus defining epiclones. We show that epiclones may transcend copy number determined clonal lineages, thus opening this important form of clonal analysis in cancer. Epiclomal is written in R and Python and is available at https://github.com/shahcompbio/Epiclomal.

Abstract PD4-02: Canadian cancer trials group trial IND.231: A phase 1 trial evaluating CX-5461, a novel first-in-class G-quadruplex stabilizer in patients with advanced solid tumors enriched for DNA-repair deficiencies

Abstract: Background: G-quadruplexes are secondary DNA structures that reversibly form in guanine-rich regions within DNA. Our group has demonstrated that CX-5461 selectively binds and stabilizes G-quadruplex structures, causing replication fork collapse and double-stranded DNA breaks. In BRCA1/2 deficient cell lines and xenograft models, synthetic lethality was observed. We are conducting a phase I study of CX-5461, a first-in-class G-quadruplex stabilizer, in patients with advanced solid tumors enriching for patients with DNA-repair deficiencies. Methods: We conducted a phase I study of 10 dose levels of CX-5461. The initial 7 dose levels (50, 100, 150, 200, 250, 325, 475 mg/m2) were administered intravenously on days 1 and 8 of a 4-week cycle, while the final 3 dose levels (325, 475 and 650 mg/m2) were administered intravenously on days 1, 8 and 15 of a 4-week cycle. Escalation was performed using a 3+3 design. Eligible patients must have advanced disease, a PS 0-2 and adequate organ function. Patients were treated until disease progression. The primary objective was the determination of RP2D. The DLT evaluation period was cycle 1 and AEs needed to be maximally managed to be considered a DLT. Secondary objectives include ORR (RECIST 1.1), PK, and toxicity (CTCAEv4.0). Results: As of March 28th, 2019, 40 patients were registered on protocol with 39 patients evaluable for toxicity and 35 patients evaluable for response. 18 of the participants were diagnosed with metastatic breast cancer. Of the evaluable patients, the median age is 53 with 24 patients having 3 or more prior regimens for their disease. There have been no DLTs observed to date. There were 5 treatment-related non-DLT grade 3 photosensitivity events (DL0, DL4, DL7, DL8, DL9) that were reversible and were secondary to lack of photo-protective measures. 3 SAEs were considered related to CX-5461 (photosensitivity of the skin (n=2); photosensitivity of the eyes (n=1). Treatment-related AEs ≥10% were photosensitivity of the skin (59%), photosensitivity of the eyes (21%), mucositis (15%), nausea (44%), hand-foot syndrome (23%), headache (10%) and rash (10%). The RP2D was determined to be 475 mg/m2 on days, 1, 8 and 15 of a 4-week cycle. PK appears dose-proportional for Cmax and AUC24,∞. Of the 40 patients treated on protocol, 34 have discontinued from the study either due to objective progressive disease (n=29), symptomatic progression of disease (n=4) or withdrawal of consent (n=1). In terms of best response, 4 patients, including 3 with breast cancer, have a confirmed PR (2 germline BRCA2, 1 germline BRCA2 VUS, germline PALB2) with an additional 6 patients, including 2 with breast cancer, (4 germline BRCA2, 2 somatic BRCA1/2) with SD as best response for >=4 cycles. Conclusion: CX-5461 is tolerable with preventable photosensitivity being the main toxicity. The RP2D is now identified at 475 mg/m2 on days 1, 8 and 15 of a 4-week cycle. Preliminary activity for CX-5461 has been observed in patients with HR-deficient tumors for both breast cancer and other tumor types. An expansion cohort, with mandatory tumor and skin biopsies, for patients with metastatic breast cancer with confirmed HR deficiency is currently open. Further updates will be provided. Citation Format: John Hilton, Karen Gelmon, David Cescon, Anna Tinker, Derek Jonker, Rachel Goodwin, Scott Laurie, Aaron Hansen, Samuel Aparicio, John Soong, Linda Hagerman, Hongbo Lui, Philippe Bedard, Kathleen Pritchard, Dongsheng Tu, Lesley Seymour. Canadian cancer trials group trial IND.231: A phase 1 trial evaluating CX-5461, a novel first-in-class G-quadruplex stabilizer in patients with advanced solid tumors enriched for DNA-repair deficiencies [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr PD4-02.

Single cell fitness landscapes induced by genetic and pharmacologic perturbations in cancer

Abstract: Tumour fitness landscapes underpin selection in cancer, impacting etiology, evolution and response to treatment. Progress in defining fitness landscapes has been impeded by a lack of timeseries perturbation experiments over realistic intervals at single cell resolution. We studied the nature of clonal dynamics induced by genetic and pharmacologic perturbation with a quantitative fitness model developed to ascribe quantitative selective coefficients to individual cancer clones, enable prediction of clone-specific growth potential, and forecast competitive clonal dynamics over time. We applied the model to serial single cell genome (>60,000 cells) and transcriptome (>58,000 cells) experiments ranging from 10 months to 2.5 years in duration. We found that genetic perturbation of TP53 in epithelial cell lines induces multiple forms of copy number alteration that confer increased fitness to clonal populations with measurable consequences on gene expression. In patient derived xenografts, predicted selective coefficients accurately forecasted clonal competition dynamics, that were validated with timeseries sampling of experimentally engineered mixtures of low and high fitness clones. In cisplatin-treated patient derived xenografts, the fitness landscape was inverted in a time-dependent manner, whereby a drug resistant clone emerged from a phylogenetic lineage of low fitness clones, and high fitness clones were eradicated. Moreover, clonal selection mediated reversible drug response early in the selection process, whereas late dynamics in genomically fixed clones were associated with transcriptional plasticity on a fixed clonal genotype. Together, our findings outline causal mechanisms with implication for interpreting how mutations and multi-faceted drug resistance mechanisms shape the etiology and cellular fitness of human cancers.

Higher serum PD-L1 level predicts increased overall survival with lapatinib versus trastuzumab in the CCTG MA.31 phase 3 trial.

Abstract: Background The purpose of this retrospective biomarker study of the Canadian Cancer Trials Group (CCTG) MA.31 randomized phase 3 trial (lapatinib vs trastuzumab) of HER2-positive metastatic breast cancer (MBC) was to evaluate the prognostic and predictive biomarker utility of pretreatment serum programmed death ligand 1 (PD-L1) levels. Methods CCTG MA.31 accrued 652 HER2-positive patients; 387 had serum available (185 in the trastuzumab arm and 202 in the lapatinib arm). The Ella immunoassay platform (ProteinSimple, San Jose, California) was used to quantitate serum PD-L1 levels. Stepwise forward Cox multivariable analyses were performed for progression-free survival and overall survival (OS). Results In the whole trial population, continuous pretreatment serum PD-L1 levels were not associated with OS. However, within the trastuzumab arm, a higher continuous pretreatment serum PD-L1 level was significant for shorter OS (hazard ratio [HR], 3.85; P = .04), but within the lapatinib arm, pretreatment serum PD-L1 was not associated with OS (P = .37). In the whole trial, in a multivariable analysis for OS, serum PD-L1 (median cut point) remained a significant independent covariate (HR, 2.38; P = .001). There was a significant interaction between treatment arm and continuous serum PD-L1 (bootstrap method; P = .0025): at or above 214.2 pg/mL (the 89th percentile), serum PD-L1 was associated with significantly shorter OS with trastuzumab treatment versus lapatinib treatment. Conclusions In the CCTG MA.31 trial, serum PD-L1 was a significant predictive factor: a higher pretreatment serum PD-L1 level was associated with shorter OS with trastuzumab treatment but with longer OS with lapatinib treatment. Immune evasion may decrease the effectiveness of trastuzumab therapy. Further evaluation of elevated serum PD-L1 in advanced breast cancer is warranted to identify patients with HER2-positive MBC who may benefit from novel immune-targeted therapies in addition to trastuzumab.