Hallmarks of transcriptional intratumour heterogeneity across a thousand tumours.

Each tumour contains diverse cellular states that underlie intratumour heterogeneity (ITH), a central challenge of cancer therapeutics1. Dozens of recent studies have begun to describe ITH by single-cell RNA sequencing, but each study typically profiled only a small number of tumours and provided a narrow view of transcriptional ITH2. Here we curate, annotate and integrate the data from 77 different studies to reveal the patterns of transcriptional ITH across 1,163 tumour samples covering 24 tumour types. Among the malignant cells, we identify 41 consensus meta-programs, each consisting of dozens of genes that are coordinately upregulated in subpopulations of cells within many tumours. The meta-programs cover diverse cellular processes including both generic (for example, cell cycle and stress) and lineage-specific patterns that we map into 11 hallmarks of transcriptional ITH. Most meta-programs of carcinoma cells are similar to those identified in non-malignant epithelial cells, suggesting that a large fraction of malignant ITH programs are variable even before oncogenesis, reflecting the biology of their cell of origin. We further extended the meta-program analysis to six common non-malignant cell types and utilize these to map cell-cell interactions within the tumour microenvironment. In summary, we have assembled a comprehensive pan-cancer single-cell RNA-sequencing dataset, which is available through the Curated Cancer Cell Atlas website, and leveraged this dataset to carry out a systematic characterization of transcriptional ITH.

RBFOX2 modulates a metastatic signature of alternative splicing in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDA) is characterized by aggressive local invasion and metastatic spread, leading to high lethality. Although driver gene mutations during PDA progression are conserved, no specific mutation is correlated with the dissemination of metastases1-3. Here we analysed RNA splicing data of a large cohort of primary and metastatic PDA tumours to identify differentially spliced events that correlate with PDA progression. De novo motif analysis of these events detected enrichment of motifs with high similarity to the RBFOX2 motif. Overexpression of RBFOX2 in a patient-derived xenograft (PDX) metastatic PDA cell line drastically reduced the metastatic potential of these cells in vitro and in vivo, whereas depletion of RBFOX2 in primary pancreatic tumour cell lines increased the metastatic potential of these cells. These findings support the role of RBFOX2 as a potent metastatic suppressor in PDA. RNA-sequencing and splicing analysis of RBFOX2 target genes revealed enrichment of genes in the RHO GTPase pathways, suggesting a role of RBFOX2 splicing activity in cytoskeletal organization and focal adhesion formation. Modulation of RBFOX2-regulated splicing events, such as via myosin phosphatase RHO-interacting protein (MPRIP), is associated with PDA metastases, altered cytoskeletal organization and the induction of focal adhesion formation. Our results implicate the splicing-regulatory function of RBFOX2 as a tumour suppressor in PDA and suggest a therapeutic approach for metastatic PDA.

Genomic Features and Classification of Homologous Recombination Deficient Pancreatic Ductal Adenocarcinoma.

BACKGROUND AND AIMS: Homologous recombination deficiency (HRD) in pancreatic ductal adenocarcinoma (PDAC), remains poorly defined beyond germline (g) alterations in BRCA1, BRCA2, and PALB2. METHODS: We interrogated whole genome sequencing (WGS) data on 391 patients, including 49 carriers of pathogenic variants (PVs) in gBRCA and PALB2. HRD classifiers were applied to the dataset and included (1) the genomic instability score (GIS) used by Myriad's MyChoice HRD assay; (2) substitution base signature 3 (SBS3); (3) HRDetect; and (4) structural variant (SV) burden. Clinical outcomes and responses to chemotherapy were correlated with HRD status. RESULTS: Biallelic tumor inactivation of gBRCA or PALB2 was evident in 43 of 49 germline carriers identifying HRD-PDAC. HRDetect (score ≥0.7) predicted gBRCA1/PALB2 deficiency with highest sensitivity (98%) and specificity (100%). HRD genomic tumor classifiers suggested that 7% to 10% of PDACs that do not harbor gBRCA/PALB2 have features of HRD. Of the somatic HRDetecthi cases, 69% were attributed to alterations in BRCA1/2, PALB2, RAD51C/D, and XRCC2, and a tandem duplicator phenotype. TP53 loss was more common in BRCA1- compared with BRCA2-associated HRD-PDAC. HRD status was not prognostic in resected PDAC; however in advanced disease the GIS (P = .02), SBS3 (P = .03), and HRDetect score (P = .005) were predictive of platinum response and superior survival. PVs in gATM (n = 6) or gCHEK2 (n = 2) did not result in HRD-PDAC by any of the classifiers. In 4 patients, BRCA2 reversion mutations associated with platinum resistance. CONCLUSIONS: Germline and parallel somatic profiling of PDAC outperforms germline testing alone in identifying HRD-PDAC. An additional 7% to 10% of patients without gBRCA/PALB2 mutations may benefit from DNA damage response agents.

Recapitulating the clinical scenario of BRCA-associated pancreatic cancer in pre-clinical models.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignancies. BRCA-associated PDAC comprises a clinically relevant subtype. A portion of these patients are highly susceptible to DNA damaging therapeutics, however, responses are heterogeneous and clinical resistance evolves. We have developed unique patient-derived xenograft (PDX) models from metastatic lesions of germline BRCA-mutated patients obtained at distinct time points; before treatment and at progression. Thus, closely mimicking clinical scenarios, to further investigate treatment naïve and resistant patients. DNA was isolated from six BRCA-mutated PDXs and classified by whole-genome sequencing to stable-genome or homologous recombination deficient (HRD)-genome. The sensitivity to DNA-damaging agents was evaluated in vivo in three BRCA-associated PDAC PDXs models: (1) HRD-genome naïve to treatments; (2) stable-genome naïve to treatment; (3) HRD-genome resistant to treatment. Correlation between disease course at tissue acquisition and response to PARP inhibitor (PARPi)/platinum was demonstrated in PDXs in vivo. Only the HRD-genome PDX, naïve to treatment, was sensitive to PARP inhibitor/cisplatin treatments. Our results demonstrate heterogeneous responses to DNA damaging agents/PARPi in BRCA-associated PDX thus reflecting the wide clinical spectrum. An HRD-genome PDX generated from a naïve to treatment biopsy was sensitive to platinum/PARPi whereas no benefit was observed in treating a HRD-genome PDXs generated from a patient that had acquired resistance nor stable-genome PDXs.

A deep-learning framework to predict cancer treatment response from histopathology images through imputed transcriptomics.

Advances in artificial intelligence have paved the way for leveraging hematoxylin and eosin-stained tumor slides for precision oncology. We present ENLIGHT-DeepPT, an indirect two-step approach consisting of (1) DeepPT, a deep-learning framework that predicts genome-wide tumor mRNA expression from slides, and (2) ENLIGHT, which predicts response to targeted and immune therapies from the inferred expression values. We show that DeepPT successfully predicts transcriptomics in all 16 The Cancer Genome Atlas cohorts tested and generalizes well to two independent datasets. ENLIGHT-DeepPT successfully predicts true responders in five independent patient cohorts involving four different treatments spanning six cancer types, with an overall odds ratio of 2.28 and a 39.5% increased response rate among predicted responders versus the baseline rate. Notably, its prediction accuracy, obtained without any training on the treatment data, is comparable to that achieved by directly predicting the response from the images, which requires specific training on the treatment evaluation cohorts.

Early Infiltration of Innate Immune Cells to the Liver Depletes HNF4α and Promotes Extrahepatic Carcinogenesis.

Multiple studies have identified metabolic changes within the tumor and its microenvironment during carcinogenesis. Yet, the mechanisms by which tumors affect the host metabolism are unclear. We find that systemic inflammation induced by cancer leads to liver infiltration of myeloid cells at early extrahepatic carcinogenesis. The infiltrating immune cells via IL6-pSTAT3 immune-hepatocyte cross-talk cause the depletion of a master metabolic regulator, HNF4α, consequently leading to systemic metabolic changes that promote breast and pancreatic cancer proliferation and a worse outcome. Preserving HNF4α levels maintains liver metabolism and restricts carcinogenesis. Standard liver biochemical tests can identify early metabolic changes and predict patients' outcomes and weight loss. Thus, the tumor induces early metabolic changes in its macroenvironment with diagnostic and potentially therapeutic implications for the host. SIGNIFICANCE: Cancer growth requires a permanent nutrient supply starting from early disease stages. We find that the tumor extends its effect to the host's liver to obtain nutrients and rewires the systemic and tissue-specific metabolism early during carcinogenesis. Preserving liver metabolism restricts tumor growth and improves cancer outcomes. This article is highlighted in the In This Issue feature, p. 1501.

A clinical evaluation of an ex vivo organ culture system to predict patient response to cancer therapy.

Introduction: Ex vivo organ cultures (EVOC) were recently optimized to sustain cancer tissue for 5 days with its complete microenvironment. We examined the ability of an EVOC platform to predict patient response to cancer therapy. Methods: A multicenter, prospective, single-arm observational trial. Samples were obtained from patients with newly diagnosed bladder cancer who underwent transurethral resection of bladder tumor and from core needle biopsies of patients with metastatic cancer. The tumors were cut into 250 µM slices and cultured within 24 h, then incubated for 96 h with vehicle or intended to treat drug. The cultures were then fixed and stained to analyze their morphology and cell viability. Each EVOC was given a score based on cell viability, level of damage, and Ki67 proliferation, and the scores were correlated with the patients' clinical response assessed by pathology or Response Evaluation Criteria in Solid Tumors (RECIST). Results: The cancer tissue and microenvironment, including endothelial and immune cells, were preserved at high viability with continued cell division for 5 days, demonstrating active cell signaling dynamics. A total of 34 cancer samples were tested by the platform and were correlated with clinical results. A higher EVOC score was correlated with better clinical response. The EVOC system showed a predictive specificity of 77.7% (7/9, 95% CI 0.4-0.97) and a sensitivity of 96% (24/25, 95% CI 0.80-0.99). Conclusion: EVOC cultured for 5 days showed high sensitivity and specificity for predicting clinical response to therapy among patients with muscle-invasive bladder cancer and other solid tumors.

Spectrum of Response to Platinum and PARP Inhibitors in Germline BRCA-Associated Pancreatic Cancer in the Clinical and Preclinical Setting.

Germline BRCA-associated pancreatic ductal adenocarcinoma (glBRCA PDAC) tumors are susceptible to platinum and PARP inhibition. The clinical outcomes of 125 patients with glBRCA PDAC were stratified based on the spectrum of response to platinum/PARP inhibition: (i) refractory [overall survival (OS) <6 months], (ii) durable response followed by acquired resistance (OS <36 months), and (iii) long-term responders (OS >36 months). Patient-derived xenografts (PDX) were generated from 25 patients with glBRCA PDAC at different clinical time points. Response to platinum/PARP inhibition in vivo and ex vivo culture (EVOC) correlated with clinical response. We deciphered the mechanisms of resistance in glBRCA PDAC and identified homologous recombination (HR) proficiency and secondary mutations restoring partial functionality as the most dominant resistant mechanism. Yet, a subset of HR-deficient (HRD) patients demonstrated clinical resistance. Their tumors displayed basal-like molecular subtype and were more aneuploid. Tumor mutational burden was high in HRD PDAC and significantly higher in tumors with secondary mutations. Anti-PD-1 attenuated tumor growth in a novel humanized glBRCA PDAC PDX model. This work demonstrates the utility of preclinical models, including EVOC, to predict the response of glBRCA PDAC to treatment, which has the potential to inform time-sensitive medical decisions. SIGNIFICANCE: glBRCA PDAC has a favorable response to platinum/PARP inhibition. However, most patients develop resistance. Additional treatment options for this unique subpopulation are needed. We generated model systems in PDXs and an ex vivo system (EVOC) that faithfully recapitulate these specific clinical scenarios as a platform to investigate the mechanisms of resistance for further drug development. This article is highlighted in the In This Issue feature, p. 1749.

Prediction of cancer treatment response from histopathology images through imputed transcriptomics.

Advances in artificial intelligence have paved the way for leveraging hematoxylin and eosin (H&E)-stained tumor slides for precision oncology. We present ENLIGHT-DeepPT, an approach for predicting response to multiple targeted and immunotherapies from H&E-slides. In difference from existing approaches that aim to predict treatment response directly from the slides, ENLIGHT-DeepPT is an indirect two-step approach consisting of (1) DeepPT, a new deep-learning framework that predicts genome-wide tumor mRNA expression from slides, and (2) ENLIGHT, which predicts response based on the DeepPT inferred expression values. DeepPT successfully predicts transcriptomics in all 16 TCGA cohorts tested and generalizes well to two independent datasets. Our key contribution is showing that ENLIGHT-DeepPT successfully predicts true responders in five independent patients' cohorts involving four different treatments spanning six cancer types with an overall odds ratio of 2.44, increasing the baseline response rate by 43.47% among predicted responders, without the need for any treatment data for training. Furthermore, its prediction accuracy on these datasets is comparable to a supervised approach predicting the response directly from the images, which needs to be trained and tested on the same cohort. ENLIGHT-DeepPT future application could provide clinicians with rapid treatment recommendations to an array of different therapies and importantly, may contribute to advancing precision oncology in developing countries.

Patient-derived xenograft models of BRCA-associated pancreatic cancers.

Pancreatic ductal adenocarcinoma (PDAC) is a dismal disease. The majority of patients diagnosed at an advanced, metastatic stage, and poor overall survival rates. The most clinically meaningful subtype obtained from PDAC genomic classification is represented by unstable genomes, and co-segregated with inactivation of DNA damage repair genes, e.g., Breast cancer 1/2 (BRCA1/2). The FDA and EMA has recently approved olaparib, a Poly (ADP-ribose) polymerase (PARP) inhibitor, as a maintenance strategy for platinum-sensitive advanced PDAC patients with BRCA mutations. However, susceptibility to treatment varies, and resistance may develop. Resistance can be defined as innate or acquired resistance to platinum/PARP-inhibition. Patient-derived xenograft (PDX) models have been utilized in cancer research for many years. We generated a unique PDX model, obtained from BRCA-associated PDAC patients at distinct time points of the disease recapitulating the different clinical scenario. In this review we discuss the relevant PDX-derived models for investigating BRCA-associated PDAC and drug development.

Immune co-culture cell microarray - a feasible tool for high-throughput functional investigation of lymphocyte-cancer interactions.

Omics analyses often result in dozens to hundreds of potential targets, requiring validation for their biological relevance. Current high-throughput functional investigation methods are frequently labor-intensive, expensive, and display low reproducibility. The Immune Co-Culture Cell Microarray (ICCM) is a formalin-fixed paraffin-embedded cell block microarray based on co-cultures of patient-derived tumor-infiltrating lymphocytes and their autologous melanoma cells. Each ICCM slide represents the same experiment and can be stained using standard immunohistochemistry and immunofluorescence techniques. Functional dynamics assessment of both proteins and microRNAs using ICCM stained slides demonstrated similar findings to flow cytometry assays and to previously published patient-derived biopsy reports.

A Preclinical Trial and Molecularly Annotated Patient Cohort Identify Predictive Biomarkers in Homologous Recombination-deficient Pancreatic Cancer.

PURPOSE: Pancreatic ductal adenocarcinoma (PDAC) arising in patients with a germline BRCA1 or BRCA2 (gBRCA) mutation may be sensitive to platinum and PARP inhibitors (PARPi). However, treatment stratification based on gBRCA mutational status alone is associated with heterogeneous responses. EXPERIMENTAL DESIGN: We performed a seven-arm preclinical trial consisting of 471 mice, representing 12 unique PDAC patient-derived xenografts, of which nine were gBRCA mutated. From 179 patients whose PDAC was whole-genome and transcriptome sequenced, we identified 21 cases with homologous recombination deficiency (HRD), and investigated prognostic biomarkers. RESULTS: We found that biallelic inactivation of BRCA1/BRCA2 is associated with genomic hallmarks of HRD and required for cisplatin and talazoparib (PARPi) sensitivity. However, HRD genomic hallmarks persisted in xenografts despite the emergence of therapy resistance, indicating the presence of a genomic scar. We identified tumor polyploidy and a low Ki67 index as predictors of poor cisplatin and talazoparib response. In patients with HRD PDAC, tumor polyploidy and a basal-like transcriptomic subtype were independent predictors of shorter survival. To facilitate clinical assignment of transcriptomic subtype, we developed a novel pragmatic two-marker assay (GATA6:KRT17). CONCLUSIONS: In summary, we propose a predictive and prognostic model of gBRCA-mutated PDAC on the basis of HRD genomic hallmarks, Ki67 index, tumor ploidy, and transcriptomic subtype.

Poly (ADP) Ribose Glycohydrolase Can Be Effectively Targeted in Pancreatic Cancer.

Patients with metastatic pancreatic ductal adenocarcinoma (PDAC) have an average survival of less than 1 year, underscoring the importance of evaluating novel targets with matched targeted agents. We recently identified that poly (ADP) ribose glycohydrolase (PARG) is a strong candidate target due to its dependence on the pro-oncogenic mRNA stability factor HuR (ELAVL1). Here, we evaluated PARG as a target in PDAC models using both genetic silencing of PARG and established small-molecule PARG inhibitors (PARGi), PDDX-01/04. Homologous repair-deficient cells compared with homologous repair-proficient cells were more sensitive to PARGi in vitro. In vivo, silencing of PARG significantly decreased tumor growth. PARGi synergized with DNA-damaging agents (i.e., oxaliplatin and 5-fluorouracil), but not with PARPi therapy. Mechanistically, combined PARGi and oxaliplatin treatment led to persistence of detrimental PARylation, increased expression of cleaved caspase-3, and increased γH2AX foci. In summary, these data validate PARG as a relevant target in PDAC and establish current therapies that synergize with PARGi. SIGNIFICANCE: PARG is a potential target in pancreatic cancer as a single-agent anticancer therapy or in combination with current standard of care.

Harnessing synthetic lethality to predict the response to cancer treatment.

While synthetic lethality (SL) holds promise in developing effective cancer therapies, SL candidates found via experimental screens often have limited translational value. Here we present a data-driven approach, ISLE (identification of clinically relevant synthetic lethality), that mines TCGA cohort to identify the most likely clinically relevant SL interactions (cSLi) from a given candidate set of lab-screened SLi. We first validate ISLE via a benchmark of large-scale drug response screens and by predicting drug efficacy in mouse xenograft models. We then experimentally test a select set of predicted cSLi via new screening experiments, validating their predicted context-specific sensitivity in hypoxic vs normoxic conditions and demonstrating cSLi's utility in predicting synergistic drug combinations. We show that cSLi can successfully predict patients' drug treatment response and provide patient stratification signatures. ISLE thus complements existing actionable mutation-based methods for precision cancer therapy, offering an opportunity to expand its scope to the whole genome.

Pancreatic cancer ascites xenograft-an expeditious model mirroring advanced therapeutic resistant disease.

Pancreatic ductal adenocarcinoma has limited treatment options. There is an urgent need for developing appropriate pre-clinical models recapitulating metastatic disease, the most common clinical scenario at presentation. Ascites accumulation occurs in up to 20-30% of patients with pancreatic cancer; this milieu represents a highly cellular research resource of metastatic peritoneal spread. In this study, we utilized pancreatic ascites/pleural effusion cancer cells to establish patient derived xenografts.Ascites/pleural effusion-patient derived xenografts were established from twelve independent cases. Xenografts were serially passed in nude mice and tissue bio-specimen banking has been established. Histopathology of emergent tumors demonstrates poorly to moderately differentiated, glandular and mucin producing tumors, mirroring morphology of primary pancreatic cancer tumors. Whole genome sequencing of six patient derived xenografts samples demonstrates common mutations and structural variations similar to those reported in primary pancreatic cancer. Xenograft tumors were dissociated to single-cells and in-vitro drug sensitivity screen assays demonstrated chemo-resistance, correlating with patient clinical scenarios, thus serving as a platform for clinically relevant translational research.Therefore, establishment of this novel ascites/pleural effusion patient derived xenograft model, with extensive histopathology and genomic characterization, opens an opportunity for the study of advanced aggressive pancreatic cancer. Characterization of metastatic disease and mechanisms of resistance to therapeutics may lead to the development of novel drug combinations.

TL-118 and gemcitabine drug combination display therapeutic efficacy in a MYCN amplified orthotopic neuroblastoma murine model--evaluation by MRI.

Neuroblastoma (NB) is the most common extra-cranial pediatric solid tumor with up to 50% of NB patients classified as having high-risk disease with poor long-term survival rates. The poor clinical outcome and aggressiveness of high-risk NB strongly correlates with enhanced angiogenesis, suggesting anti-angiogenic agents as attractive additions to the currently insufficient therapeutics. TL-118, a novel drug combination has been recently developed to inhibit tumor angiogenesis. In the current study, we used the SK-N-BE (2) cell line to generate orthotopic NB tumors in order to study the combinational therapeutic potential of TL-118 with either Gemcitabine (40 mg/kg; IP) or Retinoic acid (40 mg/kg; IP). We show that TL-118 treatment (n = 9) significantly inhibited tumor growth, increased cell apoptosis, reduced proliferation and extended mouse survival. Moreover, the reciprocal effect of TL-118 and Gemcitabine treatment (n = 10) demonstrated improved anti-tumor activity. The synergistic effect of these drugs in combination was more effective than either TL or Gemcitabine alone (n = 9), via significantly reduced cell proliferation (p<0.005), increased apoptosis (p<0.05) and significantly prolonged survival (2-fold; p<0.00001). To conclude, we demonstrate that the novel drug combination TL-118 has the ability to suppress the growth of an aggressive NB tumor. The promising results with TL-118 in this aggressive animal model may imply that this drug combination has therapeutic potential in the clinical setting.