Spatially confined sub-tumor microenvironments in pancreatic cancer.

Intratumoral heterogeneity is a critical frontier in understanding how the tumor microenvironment (TME) propels malignant progression. Here, we deconvolute the human pancreatic TME through large-scale integration of histology-guided regional multiOMICs with clinical data and patient-derived preclinical models. We discover "subTMEs," histologically definable tissue states anchored in fibroblast plasticity, with regional relationships to tumor immunity, subtypes, differentiation, and treatment response. "Reactive" subTMEs rich in complex but functionally coordinated fibroblast communities were immune hot and inhabited by aggressive tumor cell phenotypes. The matrix-rich "deserted" subTMEs harbored fewer activated fibroblasts and tumor-suppressive features yet were markedly chemoprotective and enriched upon chemotherapy. SubTMEs originated in fibroblast differentiation trajectories, and transitory states were notable both in single-cell transcriptomics and in situ. The intratumoral co-occurrence of subTMEs produced patient-specific phenotypic and computationally predictable heterogeneity tightly linked to malignant biology. Therefore, heterogeneity within the plentiful, notorious pancreatic TME is not random but marks fundamental tissue organizational units.

Tryptophan-derived microbial metabolites activate the aryl hydrocarbon receptor in tumor-associated macrophages to suppress anti-tumor immunity.

The aryl hydrocarbon receptor (AhR) is a sensor of products of tryptophan metabolism and a potent modulator of immunity. Here, we examined the impact of AhR in tumor-associated macrophage (TAM) function in pancreatic ductal adenocarcinoma (PDAC). TAMs exhibited high AhR activity and Ahr-deficient macrophages developed an inflammatory phenotype. Deletion of Ahr in myeloid cells or pharmacologic inhibition of AhR reduced PDAC growth, improved efficacy of immune checkpoint blockade, and increased intra-tumoral frequencies of IFNγ+CD8+ T cells. Macrophage tryptophan metabolism was not required for this effect. Rather, macrophage AhR activity was dependent on Lactobacillus metabolization of dietary tryptophan to indoles. Removal of dietary tryptophan reduced TAM AhR activity and promoted intra-tumoral accumulation of TNFα+IFNγ+CD8+ T cells; provision of dietary indoles blocked this effect. In patients with PDAC, high AHR expression associated with rapid disease progression and mortality, as well as with an immune-suppressive TAM phenotype, suggesting conservation of this regulatory axis in human disease.

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.

Hourglass, a rapid analysis framework for heterogeneous bioimaging data, identifies sex disparity in IL-6/STAT3-associated immune phenotypes in pancreatic cancer.

Integration and mining of bioimaging data remains a challenge and lags behind the rapidly expanding digital pathology field. We introduce Hourglass, an open-access analytical framework that streamlines biology-driven visualization, interrogation, and statistical assessment of multiparametric datasets. Cognizant of tissue and clinical heterogeneity, Hourglass systematically organizes observations across spatial and global levels and within patient subgroups. Applied to an extensive bioimaging dataset, Hourglass promptly consolidated a breadth of known interleukin-6 (IL-6) functions via its downstream effector STAT3 and uncovered a so-far unknown sexual dimorphism in the IL-6/STAT3-linked intratumoral T-cell response in human pancreatic cancer. As an R package and cross-platform application, Hourglass facilitates knowledge extraction from multi-layered bioimaging datasets for users with or without computational proficiency and provides unique and widely accessible analytical means to harness insights hidden within heterogeneous tissues at the sample and patient level. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

GATA4 and GATA6 loss-of-expression is associated with extinction of the classical programme and poor outcome in pancreatic ductal adenocarcinoma.

OBJECTIVE: GATA6 is a key regulator of the classical phenotype in pancreatic ductal adenocarcinoma (PDAC). Low GATA6 expression associates with poor patient outcome. GATA4 is the second most expressed GATA factor in the pancreas. We assessed whether, and how, GATA4 contributes to PDAC phenotype and analysed the association of expression with outcome and response to chemotherapy. DESIGN: We analysed PDAC transcriptomic data, stratifying cases according to GATA4 and GATA6 expression and identified differentially expressed genes and pathways. The genome-wide distribution of GATA4 was assessed, as well as the effects of GATA4 knockdown. A multicentre tissue microarray study to assess GATA4 and GATA6 expression in samples (n=745) from patients with resectable was performed. GATA4 and GATA6 levels were dichotomised into high/low categorical variables; association with outcome was assessed using univariable and multivariable Cox regression models. RESULTS: GATA4 messenger RNA is enriched in classical, compared with basal-like tumours. We classified samples in 4 groups as high/low for GATA4 and GATA6. Reduced expression of GATA4 had a minor transcriptional impact but low expression of GATA4 enhanced the effects of GATA6 low expression. GATA4 and GATA6 display a partially overlapping genome-wide distribution, mainly at promoters. Reduced expression of both proteins in tumours was associated with the worst patient survival. GATA4 and GATA6 expression significantly decreased in metastases and negatively correlated with basal markers. CONCLUSIONS: GATA4 and GATA6 cooperate to maintain the classical phenotype. Our findings provide compelling rationale to assess their expression as biomarkers of poor prognosis and therapeutic response.

Systemic inflammatory prognostic scores in advanced pancreatic adenocarcinoma.

BACKGROUND: Systemic inflammatory scores may aid prognostication and patient selection for trials. We compared five scores in advanced pancreatic adenocarcinoma (PDAC). METHODS: Unresectable/metastatic PDAC patients enrolled in the Comprehensive Molecular Characterisation of Advanced Pancreatic Ductal Adenocarcinoma for Better Treatment Selection trial (NCT02750657) were included. Patients had pre-treatment biopsies for whole genome and RNA sequencing. CD8 immunohistochemistry was available in a subset. The neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio, Prognostic Nutritional Index, Gustave Roussy Immune Score (GRIm-S), and Memorial Sloan Kettering Prognostic Score (MPS) were calculated. Overall survival (OS) was estimated using Kaplan-Meier methods. Associations between inflammatory scores, clinical/genomic characteristics, and OS were analysed. RESULTS: We analysed 263 patients. High-risk NLR, GRIm-S and MPS were poorly prognostic. The GRIm-S had the highest predictive ability: median OS 6.4 vs. 10 months for high risk vs. low-risk (P < 0.001); HR 2.26 (P < 0.001). ECOG ≥ 1, the basal-like subtype, and low-HRDetect were additional poor prognostic factors (P < 0.01). Inflammatory scores did not associate with RNA-based classifiers or homologous recombination repair deficiency genotypes. High-risk MPS (P = 0.04) and GRIm-S (P = 0.02) patients had lower median CD8 + tumour-infiltrating lymphocytes. CONCLUSIONS: Inflammatory scores incorporating NLR have prognostic value in advanced PDAC. Understanding immunophenotypes of poor-risk patients and using these scores in trials will advance the field.

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.

A renewed model of pancreatic cancer evolution based on genomic rearrangement patterns.

Pancreatic cancer, a highly aggressive tumour type with uniformly poor prognosis, exemplifies the classically held view of stepwise cancer development. The current model of tumorigenesis, based on analyses of precursor lesions, termed pancreatic intraepithelial neoplasm (PanINs) lesions, makes two predictions: first, that pancreatic cancer develops through a particular sequence of genetic alterations (KRAS, followed by CDKN2A, then TP53 and SMAD4); and second, that the evolutionary trajectory of pancreatic cancer progression is gradual because each alteration is acquired independently. A shortcoming of this model is that clonally expanded precursor lesions do not always belong to the tumour lineage, indicating that the evolutionary trajectory of the tumour lineage and precursor lesions can be divergent. This prevailing model of tumorigenesis has contributed to the clinical notion that pancreatic cancer evolves slowly and presents at a late stage. However, the propensity for this disease to rapidly metastasize and the inability to improve patient outcomes, despite efforts aimed at early detection, suggest that pancreatic cancer progression is not gradual. Here, using newly developed informatics tools, we tracked changes in DNA copy number and their associated rearrangements in tumour-enriched genomes and found that pancreatic cancer tumorigenesis is neither gradual nor follows the accepted mutation order. Two-thirds of tumours harbour complex rearrangement patterns associated with mitotic errors, consistent with punctuated equilibrium as the principal evolutionary trajectory. In a subset of cases, the consequence of such errors is the simultaneous, rather than sequential, knockout of canonical preneoplastic genetic drivers that are likely to set-off invasive cancer growth. These findings challenge the current progression model of pancreatic cancer and provide insights into the mutational processes that give rise to these aggressive tumours.

Clinical and genomic characterisation of mismatch repair deficient pancreatic adenocarcinoma.

OBJECTIVE: To describe the clinical, pathological and genomic characteristics of pancreatic cancer with DNA mismatch repair deficiency (MMRD) and proficiency (MMRP). DESIGN: We identified patients with MMRD and MMRP pancreatic cancer in a clinical cohort (N=1213, 519 with genetic testing, 53 with immunohistochemistry (IHC)) and a genomic cohort (N=288 with whole-genome sequencing (WGS)). RESULTS: 12 out of 1213 (1.0%) in the clinical cohort were MMRD by IHC or WGS. Of the 14 patients with Lynch syndrome, 3 (21.4%) had an MMRP pancreatic cancer by IHC, and 4 (28.6%) were excluded because tissue was unavailable for testing. MMRD cancers had longer overall survival after surgery (weighted HR after coarsened exact matching 0.11, 95% CI 0.02 to 0.78, p=0.001). One patient with an unresectable MMRD cancer has an ongoing partial response 3 years after starting treatment with PD-L1/CTLA-4 inhibition. This tumour showed none of the classical histopathological features of MMRD. 9 out of 288 (3.1%) tumours with WGS were MMRD. Despite markedly higher tumour mutational burden and neoantigen loads, MMRD cancers were significantly less likely to have mutations in usual pancreatic cancer driver genes like KRAS and SMAD4, but more likely to have mutations in genes that drive cancers with microsatellite instability like ACV2RA and JAK1. MMRD tumours were significantly more likely to have a basal-like transcriptional programme and elevated transcriptional markers of immunogenicity. CONCLUSIONS: MMRD pancreatic cancers have distinct clinical, pathological and genomic profiles. Patients with MMRD pancreatic cancer should be considered for basket trials targeting enhanced immunogenicity or the unique genomic drivers in these malignancies.

Whole genomes define concordance of matched primary, xenograft, and organoid models of pancreas cancer.

Pancreatic ductal adenocarcinoma (PDAC) has the worst prognosis among solid malignancies and improved therapeutic strategies are needed to improve outcomes. Patient-derived xenografts (PDX) and patient-derived organoids (PDO) serve as promising tools to identify new drugs with therapeutic potential in PDAC. For these preclinical disease models to be effective, they should both recapitulate the molecular heterogeneity of PDAC and validate patient-specific therapeutic sensitivities. To date however, deep characterization of the molecular heterogeneity of PDAC PDX and PDO models and comparison with matched human tumour remains largely unaddressed at the whole genome level. We conducted a comprehensive assessment of the genetic landscape of 16 whole-genome pairs of tumours and matched PDX, from primary PDAC and liver metastasis, including a unique cohort of 5 'trios' of matched primary tumour, PDX, and PDO. We developed a pipeline to score concordance between PDAC models and their paired human tumours for genomic events, including mutations, structural variations, and copy number variations. Tumour-model comparisons of mutations displayed single-gene concordance across major PDAC driver genes, but relatively poor agreement across the greater mutational load. Genome-wide and chromosome-centric analysis of structural variation (SV) events highlights previously unrecognized concordance across chromosomes that demonstrate clustered SV events. We found that polyploidy presented a major challenge when assessing copy number changes; however, ploidy-corrected copy number states suggest good agreement between donor-model pairs. Collectively, our investigations highlight that while PDXs and PDOs may serve as tractable and transplantable systems for probing the molecular properties of PDAC, these models may best serve selective analyses across different levels of genomic complexity.

WaveCNV: allele-specific copy number alterations in primary tumors and xenograft models from next-generation sequencing.

MOTIVATION: Copy number variations (CNVs) are a major source of genomic variability and are especially significant in cancer. Until recently microarray technologies have been used to characterize CNVs in genomes. However, advances in next-generation sequencing technology offer significant opportunities to deduce copy number directly from genome sequencing data. Unfortunately cancer genomes differ from normal genomes in several aspects that make them far less amenable to copy number detection. For example, cancer genomes are often aneuploid and an admixture of diploid/non-tumor cell fractions. Also patient-derived xenograft models can be laden with mouse contamination that strongly affects accurate assignment of copy number. Hence, there is a need to develop analytical tools that can take into account cancer-specific parameters for detecting CNVs directly from genome sequencing data. RESULTS: We have developed WaveCNV, a software package to identify copy number alterations by detecting breakpoints of CNVs using translation-invariant discrete wavelet transforms and assign digitized copy numbers to each event using next-generation sequencing data. We also assign alleles specifying the chromosomal ratio following duplication/loss. We verified copy number calls using both microarray (correlation coefficient 0.97) and quantitative polymerase chain reaction (correlation coefficient 0.94) and found them to be highly concordant. We demonstrate its utility in pancreatic primary and xenograft sequencing data. AVAILABILITY AND IMPLEMENTATION: Source code and executables are available at https://github.com/WaveCNV. The segmentation algorithm is implemented in MATLAB, and copy number assignment is implemented Perl. CONTACT: lakshmi.muthuswamy@gmail.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Cytidine deaminases APOBEC3C and APOBEC3D promote DNA replication stress resistance in pancreatic cancer cells.

Gemcitabine is a potent inhibitor of DNA replication and is a mainstay therapeutic for diverse cancers, particularly pancreatic ductal adenocarcinoma (PDAC). However, most tumors remain refractory to gemcitabine therapies. Here, to define the cancer cell response to gemcitabine, we performed genome-scale CRISPR-Cas9 chemical-genetic screens in PDAC cells and found selective loss of cell fitness upon disruption of the cytidine deaminases APOBEC3C and APOBEC3D. Following gemcitabine treatment, APOBEC3C and APOBEC3D promote DNA replication stress resistance and cell survival by deaminating cytidines in the nuclear genome to ensure DNA replication fork restart and repair in PDAC cells. We provide evidence that the chemical-genetic interaction between APOBEC3C or APOBEC3D and gemcitabine is absent in nontransformed cells but is recapitulated across different PDAC cell lines, in PDAC organoids and in PDAC xenografts. Thus, we uncover roles for APOBEC3C and APOBEC3D in DNA replication stress resistance and offer plausible targets for improving gemcitabine-based therapies for PDAC.

In vivo CRISPR screens reveal SCAF1 and USP15 as drivers of pancreatic cancer.

Functionally characterizing the genetic alterations that drive pancreatic cancer is a prerequisite for precision medicine. Here, we perform somatic CRISPR/Cas9 mutagenesis screens to assess the transforming potential of 125 recurrently mutated pancreatic cancer genes, which revealed USP15 and SCAF1 as pancreatic tumor suppressors. Mechanistically, we find that USP15 functions in a haploinsufficient manner and that loss of USP15 or SCAF1 leads to reduced inflammatory TNFα, TGF-ß and IL6 responses and increased sensitivity to PARP inhibition and Gemcitabine. Furthermore, we find that loss of SCAF1 leads to the formation of a truncated, inactive USP15 isoform at the expense of full-length USP15, functionally coupling SCAF1 and USP15. Notably, USP15 and SCAF1 alterations are observed in 31% of pancreatic cancer patients. Our results highlight the utility of in vivo CRISPR screens to integrate human cancer genomics and mouse modeling for the discovery of cancer driver genes with potential prognostic and therapeutic implications.

Crowd-sourced benchmarking of single-sample tumor subclonal reconstruction.

Subclonal reconstruction algorithms use bulk DNA sequencing data to quantify parameters of tumor evolution, allowing an assessment of how cancers initiate, progress and respond to selective pressures. We launched the ICGC-TCGA (International Cancer Genome Consortium-The Cancer Genome Atlas) DREAM Somatic Mutation Calling Tumor Heterogeneity and Evolution Challenge to benchmark existing subclonal reconstruction algorithms. This 7-year community effort used cloud computing to benchmark 31 subclonal reconstruction algorithms on 51 simulated tumors. Algorithms were scored on seven independent tasks, leading to 12,061 total runs. Algorithm choice influenced performance substantially more than tumor features but purity-adjusted read depth, copy-number state and read mappability were associated with the performance of most algorithms on most tasks. No single algorithm was a top performer for all seven tasks and existing ensemble strategies were unable to outperform the best individual methods, highlighting a key research need. All containerized methods, evaluation code and datasets are available to support further assessment of the determinants of subclonal reconstruction accuracy and development of improved methods to understand tumor evolution.

Clinically impactful metabolic subtypes of pancreatic ductal adenocarcinoma (PDAC).

Background: Pancreatic ductal adenocarcinoma (PDAC) is a lethal disease characterized by a diverse tumor microenvironment. The heterogeneous cellular composition of PDAC makes it challenging to study molecular features of tumor cells using extracts from bulk tumor. The metabolic features in tumor cells from clinical samples are poorly understood, and their impact on clinical outcomes are unknown. Our objective was to identify the metabolic features in the tumor compartment that are most clinically impactful. Methods: A computational deconvolution approach using the DeMixT algorithm was applied to bulk RNASeq data from The Cancer Genome Atlas to determine the proportion of each gene's expression that was attributable to the tumor compartment. A machine learning algorithm designed to identify features most closely associated with survival outcomes was used to identify the most clinically impactful metabolic genes. Results: Two metabolic subtypes (M1 and M2) were identified, based on the pattern of expression of the 26 most important metabolic genes. The M2 phenotype had a significantly worse survival, which was replicated in three external PDAC cohorts. This PDAC subtype was characterized by net glycogen catabolism, accelerated glycolysis, and increased proliferation and cellular migration. Single cell data demonstrated substantial intercellular heterogeneity in the metabolic features that typified this aggressive phenotype. Conclusion: By focusing on features within the tumor compartment, two novel and clinically impactful metabolic subtypes of PDAC were identified. Our study emphasizes the challenges of defining tumor phenotypes in the face of the significant intratumoral heterogeneity that typifies PDAC. Further studies are required to understand the microenvironmental factors that drive the appearance of the metabolic features characteristic of the aggressive M2 PDAC phenotype.

Molecular characterisation of pancreatic ductal adenocarcinoma with NTRK fusions and review of the literature.

AIMS: The majority of pancreatic ductal adenocarcinomas (PDACs) harbour oncogenic mutations in KRAS with variants in TP53, CDKN2A and SMAD4 also prevalent. The presence of oncogenic fusions including NTRK fusions are rare but important to identify. Here we ascertain the prevalence of NTRK fusions and document their genomic characteristics in a large series of PDAC. METHODS: Whole genome sequencing and RNAseq were performed on a series of patients with resected or locally advanced/metastatic PDAC collected between 2008 and 2020 at a single institution. A subset of specimens underwent immunohistochemistry (IHC) analysis. Clinical and molecular characterisation and IHC sensitivity and specificity were evaluated. RESULTS: 400 patients were included (resected n=167; locally advanced/metastatic n=233). Three patients were identified as harbouring an NTRK fusion, two EML4-NTRK3 (KRAS-WT) and a single novel KANK1-NTRK3 fusion. The latter occurring in the presence of a subclonal KRAS mutation. Typical PDAC drivers were present including mutations in TP53 and CDKN2A. Substitution base signatures and tumour mutational burden were similar to typical PDAC. The prevalence of NTRK fusions was 0.8% (3/400), while in KRAS wild-type tumours, it was 6.25% (2/32). DNA prediction alone documented six false-positive cases. RNA analysis correctly identified the in-frame fusion transcripts. IHC analysis was negative in the KANK1-NTRK3 fusion but positive in a EML4-NTRK3 case, highlighting lower sensitivity of IHC. CONCLUSION: NTRK fusions are rare; however, with emerging therapeutic options targeting these fusions, detection is vital. Reflex testing for KRAS mutations and subsequent RNA-based screening could help identify these cases in PDAC.

Chemokine expression predicts T cell-inflammation and improved survival with checkpoint inhibition across solid cancers.

Immune checkpoint inhibitors (ICI) are highly effective in specific cancers where canonical markers of antitumor immunity are used for patient selection. Improved predictors of T cell-inflammation are needed to identify ICI-responsive tumor subsets in additional cancer types. We investigated associations of a 4-chemokine expression signature (c-Score: CCL4, CCL5, CXCL9, CXCL10) with metrics of antitumor immunity across tumor types. Across cancer entities from The Cancer Genome Atlas, subgroups of tumors displayed high expression of the c-Score (c-Scorehi) with increased expression of immune checkpoint (IC) genes and transcriptional hallmarks of the cancer-immunity cycle. There was an incomplete association of the c-Score with high tumor mutation burden (TMB), with only 15% of c-Scorehi tumors displaying ≥10 mutations per megabase. In a heterogeneous pan-cancer cohort of 82 patients, with advanced and previously treated solid cancers, c-Scorehi tumors had a longer median time to progression (103 versus 72 days, P = 0.012) and overall survival (382 versus 196 days, P = 0.038) following ICI therapy initiation, compared to patients with low c-Score expression. We also found c-Score stratification to outperform TMB assignment for overall survival prediction (HR = 0.42 [0.22-0.79], P = 0.008 versus HR = 0.60 [0.29-1.27], P = 0.18, respectively). Assessment of the c-Score using the TIDE and PredictIO databases, which include ICI treatment outcomes from 10 tumor types, provided further support for the c-Score as a predictive ICI therapeutic biomarker. In summary, the c-Score identifies patients with hallmarks of T cell-inflammation and potential response to ICI treatment across cancer types, which is missed by TMB assignment.

Widespread hypertranscription in aggressive human cancers.

Cancers are often defined by the dysregulation of specific transcriptional programs; however, the importance of global transcriptional changes is less understood. Hypertranscription is the genome-wide increase in RNA output. Hypertranscription's prevalence, underlying drivers, and prognostic significance are undefined in primary human cancer. This is due, in part, to limitations of expression profiling methods, which assume equal RNA output between samples. Here, we developed a computational method to directly measure hypertranscription in 7494 human tumors, spanning 31 cancer types. Hypertranscription is ubiquitous across cancer, especially in aggressive disease. It defines patient subgroups with worse survival, even within well-established subtypes. Our data suggest that loss of transcriptional suppression underpins the hypertranscriptional phenotype. Single-cell analysis reveals hypertranscriptional clones, which dominate transcript production regardless of their size. Last, patients with hypertranscribed mutations have improved response to immune checkpoint therapy. Our results provide fundamental insights into gene dysregulation across human cancers and may prove useful in identifying patients who would benefit from novel therapies.

Mitochondrial fitness and cancer risk.

Changes in metabolism are a hallmark of cancer, but molecular signatures of altered bioenergetics to aid in clinical decision-making do not currently exist. We recently identified a group of human tumors with constitutively reduced expression of the mitochondrial structural protein, Mic60, also called mitofilin or inner membrane mitochondrial protein (IMMT). These Mic60-low tumors exhibit severe loss of mitochondrial fitness, paradoxically accompanied by increased metastatic propensity and upregulation of a unique transcriptome of Interferon (IFN) signaling and Senescence-Associated Secretory Phenotype (SASP). Here, we show that an optimized, 11-gene signature of Mic60-low tumors is differentially expressed in multiple malignancies, compared to normal tissues, and correlates with poor patient outcome. When analyzed in three independent patient cohorts of pancreatic ductal adenocarcinoma (PDAC), the Mic60-low gene signature was associated with aggressive disease variants, local inflammation, FOLFIRINOX failure and shortened survival, independently of age, gender, or stage. Therefore, the 11-gene Mic60-low signature may provide an easily accessible molecular tool to stratify patient risk in PDAC and potentially other malignancies.