Sarah Davidson, Mirjana Efremova, Angela Riedel, Bidesh Mahata, Jhuma Pramanik, Jani Huuhtanen, Gozde Kar, Roser Vento-Tormo, Tzachi Hagai, Xi Chen, Muzlifah A. Haniffa, Jacqueline D. Shields, Sarah A Teichmann

Non-cancerous stromal cells represent a highly diverse compartment of the tumour, yet their role across tumour evolution remains unclear. We employed single-cell RNA sequencing to determine stromal adaptations in murine melanoma at different points of tumour development. Naive lymphocytes recruited from lymph nodes underwent activation and clonal expansion within the tumour, prior to PD1 and Lag3 expression, while tumour-associated myeloid cells promoted the formation of a suppressive niche through cytokine secretion and inhibitory T cell interactions. We identified three temporally distinct cancer-associated fibroblast (CAF) populations displaying unique signatures, and verified these in human datasets. In early tumours, immune CXCL12/CSF1 and complement-expressing CAFs supported recruitment of macrophages, whereas contractile CAFs became more prevalent in later tumours. This study highlights the complex interplay and increasing diversity among cells that co-evolve with the tumour, indicating that from early stages of development, stromal cells acquire the capacity to modulate the immune landscape towards suppression.

Fredrik Salmen, Sanja Vickovic, Ludvig Larsson, Linnea Stenbeck, Johan Vallon-Christersson, Anna Ehinger, Jari Hakkinen, Åke Borg, Jonas Frisen, Patrik Stahl, Joakim Lundeberg

The comprehensive analysis of tumor tissue heterogeneity is crucial for determining specific disease states and establishing suitable treatment regimes. Here, we analyze tumor tissue sections from ten patients diagnosed with HER2+ breast cancer. We obtain and analyze multidimensional, genome-wide transcriptomics data to resolve spatial immune cell distribution and identity within the tissue sections. Furthermore, we determine the extent of immune cell infiltration in different regions of the tumor tissue, including invasive cancer regions. We combine cross-sectioning and computational alignment to build three-dimensional images of the transcriptional landscape of the tumor and its microenvironment. The three-dimensional data clearly demonstrates the heterogeneous nature of tumor-immune interactions and reveal interpatient differences in immune cell infiltration patterns. Our study shows the potential for an improved stratification and description of the tumor-immune interplay, which is likely to be essential in treatment decisions.

Yotam E Bar-Ephraim, Kai Kretzschmar, Priyanca Asra, Evelien de Jongh, Kim E Boonekamp, Jarno Drost, Joost van Gorp, Apollo Pronk, Niels Smakman, Inez J. Gan, Zsolt Sebestyen, Jurgen HE Kuball, Robert GJ Vries, Hans Clevers

Immune escape has been recognised as one of the hallmarks of cancer. Overcoming this immunomodulatory process by tumour cells has become a major therapeutic target. Here we utilize organoid technology to study immune-cancer interactions and assess immunomodulation by colorectal cancer (CRC). Transcriptional profiling and flow cytometry revealed that organoids maintain differential expression of immunomodulatory molecules present in primary tumours. Finally, we established a method to model antigen-specific epithelial cell killing and cancer immunomodulation in vitro using CRC organoids co-cultured with cytotoxic T cells. Our method may serve as a first step to rebuilding the tumor microenvironment in vitro.

Alexandre Coudray, Anna M Battenhouse, Philipp Bucher, Vishwanath R Iyer

To detect functional somatic mutations in tumor samples, whole-exome sequencing (WES) is often used for its reliability and relative low cost. RNA-seq, while generally used to measure gene expression, can potentially also be used for identification of somatic mutations. However there has been little systematic evaluation of the utility of RNA-seq for identifying somatic mutations. Here, we develop and evaluate a pipeline for processing RNA-seq data from glioblastoma multiforme (GBM) tumors in order to identify somatic mutations. The pipeline entails the use of the STAR aligner 2-pass procedure jointly with MuTect2 from GATK to detect somatic variants. Variants identified from RNA-seq data were evaluated by comparison against the COSMIC and dbSNP databases, and also compared to somatic variants identified by exome sequencing. We also estimated the putative functional impact of coding variants in the most frequently mutated genes in GBM. Interestingly, variants identified by RNA-seq alone showed better representation of GBM-related mutations cataloged by COSMIC. RNA-seq-only data substantially outperformed the ability of WES to reveal potentially new somatic mutations in known GBM-related pathways, and allowed us to build a high-quality set of somatic mutations common to exome and RNA-seq calls. Using RNA-seq data in parallel with WES data to detect somatic mutations in cancer genomes can thus broaden the scope of discoveries and lend additional support to somatic variants identified by exome sequencing alone.

Ron S Gejman, Heather F Jones, Martin G Klatt, Aaron Y Chang, Claire Y Oh, Smita S Chandran, Tatiana Korontsvit, Viktoriya Zakahleva, Tao Dao, Christopher A Klebanoff, David A Scheinberg

T cell receptor (TCR)-based therapeutic cells and agents have emerged as a new class of effective cancer therapeutics. These therapies work on cells that express intracellular cancer-associated proteins by targeting peptides displayed on major histocompatibility complex receptors. However, cross-reactivities of these agents to off-target cells and tissues have resulted in serious, sometimes fatal, adverse events. We have developed a high throughput genetic platform (termed "PresentER") that encodes MHC-I peptide minigenes for functional immunological assays as well as for determining the reactivities of TCR-like therapeutic agents against large libraries of MHC-I ligands. In this report, we demonstrate that PresentER can be used to identify the on-and-off targets of T cells and TCR mimic antibodies using in vitro co-culture assays or binding assays. We find dozens of MHC-I ligands that are cross-reactive with two TCR mimic antibodies and two native TCRs and that are not easily predictable by other methods.

Nathan Wan, David Weinberg, Tzu-Yu Liu, Katherine Niehaus, Daniel Delubac, Ajay Kannan, Brandon White, Eric A. Ariazi, Mitch Bailey, Marvin Bertin, Nathan Boley, Derek Bowen, James Cregg, Adam M. Drake, Riley Ennis, Signe Fransen, Erik Gafni, Loren Hansen, Yaping Liu, Gabriel L Otte, Jennifer Pecson, Brandon Rice, Gabriel E Sanderson, Aarushi Sharma, John St. John, Catherina Tang, Abraham Tzou, Leilani Young, Girish Putcha, Imran S. Haque

Background: Blood-based methods using cell-free DNA (cfDNA) are under development as an alternative to existing screening tests. However, early-stage detection of cancer using tumor-derived cfDNA has proven challenging because of the small proportion of cfDNA derived from tumor tissue in early-stage disease. A machine learning approach to discover signatures in cfDNA, potentially reflective of both tumor and non-tumor contributions, may represent a promising direction for the early detection of cancer. Methods: Whole-genome sequencing was performed on cfDNA extracted from plasma samples (N=546 colorectal cancer and 271 non-cancer controls). Reads aligning to protein-coding gene bodies were extracted, and read counts were normalized. cfDNA tumor fraction was estimated using IchorCNA. Machine learning models were trained using k-fold cross-validation and confounder-based cross-validation to assess generalization performance. Results: In a colorectal cancer cohort heavily weighted towards early-stage cancer (80% stage I/II), we achieved a mean AUC of 0.92 (95% CI 0.91-0.93) with a mean sensitivity of 85% (95% CI 83-86%) at 85% specificity. Sensitivity generally increased with tumor stage and increasing tumor fraction. Stratification by age, sequencing batch, and institution demonstrated the impact of these confounders and provided a more accurate assessment of generalization performance. Conclusions: A machine learning approach using cfDNA achieved high sensitivity and specificity in a large, predominantly early-stage, colorectal cancer cohort. The possibility of systematic technical and institution-specific biases warrants similar confounder analyses in other studies. Prospective validation of this machine learning method and evaluation of a multi-analyte approach are underway.

Charles P. Couturier, Shamini Ayyadhury, Phuong U. Le, Jean Monlong, Gabriele Riva, Redouane Allache, Salma Baig, Xiaohua Yan, Mathieu Bourgey, Changseok Lee, Yu Chang David Wang, V. Wee Yong, Marie-Christine Guiot, Bratislav Misic, Jack Antel, Guillaume Bourque, Jiannis Ragoussis, Kevin Petrecca

Cancer stem cells are critical for cancer initiation, development, and resistance to treatments. Our understanding of these processes, and how they relate to glioblastoma heterogeneity, is limited. To overcome these limitations, we performed single-cell RNA-sequencing on 38 296 glioblastoma cells and 22 637 normal human fetal brain cells. Using an unbiased approach, we mapped the lineage hierarchy of the developing human brain and compared the transcriptome of each cancer cell to this roadmap. We discovered a conserved neural trilineage cancer hierarchy with glial progenitor-like cells at the apex. We also found that this progenitor population contains the majority of cancer's cycling cells and is the origin of heterogeneity. Finally, we show that this hierarchal map can be used to identify therapeutic targets specific to progenitor cancer stem cells. Our analyses show that normal brain development reconciles glioblastoma development, unravels the origin of glioblastoma heterogeneity, and helps to identify cancer stem cell-specific targets.

Luiza Moore, Daniel Leongamornlert, Tim H. H. Coorens, Mathijs A Sanders, Peter Ellis, Kevin Dawson, Francesco Maura, Jyoti Nangalia, Patrick S Tarpey, Simon F Brunner, Henry Lee-Six, Raheleh Rahbari, Sarah Moody, Yvette Hooks, Krishnaa Mahbubani, Mercedes Jimenez-Linan, Jan J Brosens, Christine A. Iacobuzio-Donahue, Inigo Martincorena, Kourosh Saeb-Parsy, Peter J. Campbell, Michael R. Stratton

All normal somatic cells are thought to acquire mutations. However, characterisation of the patterns and consequences of somatic mutation in normal tissues is limited. Uterine endometrium is a dynamic tissue that undergoes cyclical shedding and reconstitution and is lined by a gland-forming epithelium. Whole genome sequencing of normal endometrial glands showed that most are clonal cell populations derived from a recent common ancestor with mutation burdens differing from other normal cell types and manyfold lower than endometrial cancers. Mutational signatures found ubiquitously account for most mutations. Many, in some women potentially all, endometrial glands are colonised by cell clones carrying driver mutations in cancer genes, often with multiple drivers. Total and driver mutation burdens increase with age but are also influenced by other factors including body mass index and parity. Clones with drivers often originate during early decades of life. The somatic mutational landscapes of normal cells differ between cell types and are revealing the procession of neoplastic change leading to cancer.

Peter Ulz, Samantha Perakis, Qing Zhou, Tina Moser, Jelena Belic, Isaac Lazzeri, Albert Wölfler, Armin Zebisch, Armin Gerger, Gunda Pristauz, Edgar Petru, Brandon White, Charles E.S. Roberts, John St. John, Michael G Schimek, Jochen B Geigl, Thomas Bauernhofer, Heinz Sill, Christoph Bock, Ellen Heitzer, Michael R Speicher

Deregulation of transcription factors (TFs) is an important driver of tumorigenesis. We developed and validated a minimally invasive method for assessing TF activity based on cell-free DNA sequencing and nucleosome footprint analysis. We analyzed whole genome sequencing data for >1,000 cell-free DNA samples from cancer patients and healthy controls using a newly developed bioinformatics pipeline that infers accessibility of TF binding sites from cell-free DNA fragmentation patterns. We observed patient-specific as well as tumor-specific patterns, including accurate prediction of tumor subtypes in prostate cancer, with important clinical implications for the management of patients. Furthermore, we show that cell-free DNA TF profiling is capable of early detection of colorectal carcinomas. Our approach for mapping tumor-specific transcription factor binding in vivo based on blood samples makes a key part of the noncoding genome amenable to clinical analysis.

Michael K Dame, Durga Attili, Shannon D McClintock, Priya H Dedhia, Peter Ouilette, Olaf Hardt, Alana M Chin, Xiang Xue, Julie Laliberte, Erica L Katz, Gina M Newsome, David R. Hill, Alyssa J. Miller, Yu-Hwai Tsai, David Agorku, Christopher H Altheim, Andreas Bosio, Becky Simon, Linda C Samuelson, Jay A Stoerker, Henry D Appelman, James Varani, Max S Wicha, Dean E Brenner, Yatrik M Shah, Jason R Spence, Justin A Colacino

The intestine is maintained by stem cells located at the base of crypts and distinguished by the expression of LGR5. Genetically engineered mouse models have provided a wealth of information about intestinal stem cells, while less is known about human intestinal stem cells due to difficulty detecting and isolating these cells. We established an organoid repository from patient-derived adenomas, adenocarcinomas, and normal colon, which we analyzed for variants in 71 colorectal cancer (CRC) associated genes. Normal and neoplastic colon tissue organoids were analyzed by immunohistochemistry and fluorescent-activated cell sorting for LGR5. LGR5-positive cells were isolated from 4 adenoma organoid lines and were subjected to RNA-sequencing. We found that LGR5 expression in the epithelium and stroma was associated with tumor stage, and by integrating functional experiments with LGR5-sorted cell RNA-seq data from adenoma and normal organoids, we found correlations between LGR5 and CRC-specific genes, including DKK4 (dickkopf WNT signaling pathway inhibitor 4) and SMOC2 (SPARC related modular calcium binding 2). Collectively, this work provides resources, methods and new markers to isolate and study stem cells in human tissue homeostasis and carcinogenesis.

Heiko Horn, Michael S Lawrence, Jessica Xin Hu, Elizabeth Worstell, Nina Ilic, Yashaswi Shrestha, Eejung Kim, Atanas Kamburov, Alireza Kashani, William C Hahn, Jesse S Boehm, Gad Getz, Kasper Lage

Heterogeneity across cancer makes it difficult to find driver genes with intermediate (2-20%) and low frequency (<2%) mutations, and we are potentially missing entire classes of networks (or pathways) of biological and therapeutic value. Here, we quantify the extent to which cancer genes across 21 tumor types have an increased burden of mutations in their immediate gene network derived from functional genomics data. We formalize a classifier that accurately calculates the significance level of a gene’s network mutation burden (NMB) and show it can accurately predict known cancer genes and recently proposed driver genes in the majority of tested tumours. Our approach predicts 62 putative cancer genes, including 35 with clear connection to cancer and 27 genes, which point to new cancer biology. NMB identifies proportionally more (4x) low-frequency mutated genes as putative cancer genes than gene-based tests, and provides molecular clues in patients without established driver mutations. Our quantitative and comparative analysis of pan-cancer networks across 21 tumour types gives new insights into the biological and genetic architecture of cancers and enables additional discovery from existing cancer genomes. The framework we present here should become increasingly useful with more sequencing data in the future.

Lina Wadi, Liis Uusküla-Reimand, Keren Isaev, Shimin Shuai, Vincent Huang, Minggao Liang, J. Drew Thompson, Yao Li, Luyao Ruan, Marta Paczkowska, Michal Krassowski, Irakli Dzneladze, Ken Kron, Alexander Murison, Parisa Mazrooei, Robert G Bristow, Jared T Simpson, Mathieu Lupien, Michael D Wilson, Lincoln D. Stein, Paul C. Boutros, Jüri Reimand

A comprehensive catalogue of the mutations that drive tumorigenesis and progression is essential to understanding tumor biology and developing therapies. Protein-coding driver mutations have been well-characterized by large exome-sequencing studies, however many tumors have no mutations in protein-coding driver genes. Non-coding mutations are thought to explain many of these cases, however few non-coding drivers besides TERT promoter are known. To fill this gap, we analyzed 150,000 cis-regulatory regions in 1,844 whole cancer genomes from the ICGC-TCGA PCAWG project. Using our new method, ActiveDriverWGS, we found 41 frequently mutated regulatory elements (FMREs) enriched in non-coding SNVs and indels (FDR<0.05) characterized by aging-associated mutation signatures and frequent structural variants. Most FMREs are distal from genes, reported here for the first time and also recovered by additional driver discovery methods. FMREs were enriched in super-enhancers, H3K27ac enhancer marks of primary tumors and long-range chromatin interactions, suggesting that the mutations drive cancer by distally controlling gene expression through three-dimensional genome organization. In support of this hypothesis, the chromatin interaction network of FMREs and target genes revealed associations of mutations and differential gene expression of known and novel cancer genes (e.g., CNNB1IP1, RCC1), activation of immune response pathways and altered enhancer marks. Thus distal genomic regions may include additional, infrequently mutated drivers that act on target genes via chromatin loops. Our study is an important step towards finding such regulatory regions and deciphering the somatic mutation landscape of the non-coding genome.

Alicia C Smart, Claire A Margolis, Harold Pimentel, Meng Xiao He, Diana Miao, Dennis Adeegbe, Tim Fugmann, Kwok-Kin Wong, Eliezer M Van Allen

Personalized cancer vaccine strategies directed at tumor neoantigens derived from somatic mutations in the DNA are currently under prospective evaluation. Alterations in tumor RNA, rather than DNA, may also represent a previously-unexplored source of neoantigens. Here, we show that intron retention, a widespread feature of cancer transcriptomes, represents a novel source of tumor neoantigens. We developed an in silico approach to identify retained intron neoantigens from RNA sequencing data and applied this methodology to tumor samples from patients with melanoma treated with immune checkpoint blockade, discovering that the retained intron neoantigen burden in these samples augments the DNA-derived, somatic neoantigen burden. We validated the existence of retained intron derived neoantigens by implementing this technique on cancer cell lines with mass spectrometry-derived immunopeptidome data, revealing that retained intron neoantigens were complexed with MHC I experimentally. Unexpectedly, we observed a trend toward lack of clinical benefit from immune checkpoint blockade in high retained intron load-tumors, which harbored transcriptional signatures consistent with cell cycle dysregulation and DNA damage repair. Our results demonstrate the contribution of transcriptional dysregulation to the overall burden of tumor neoantigens, provide a foundation for augmenting personalized cancer vaccine development with a new class of tumor neoantigens, and demonstrate how global transcriptional dysregulation may impact selective response to immune checkpoint blockade.

Julien Racle, Kaat de Jonge, Petra Baumgaertner, Daniel E. Speiser, David Gfeller

Immune cells infiltrating tumors can have important impact on tumor progression and response to therapy. We present an efficient algorithm to simultaneously estimate the fraction of cancer and immune cell types from bulk tumor gene expression data. Our method integrates novel gene expression profiles from each major non-malignant cell type found in tumors, renormalization based on cell-type specific mRNA content, and the ability to consider uncharacterized and possibly highly variable cell types. Feasibility is demonstrated by validation with flow cytometry, immunohistochemistry and single-cell RNA-Seq analyses of human melanoma and colorectal tumor specimens. Altogether, our work not only improves accuracy but also broadens the scope of absolute cell fraction predictions from tumor gene expression data, and provides a unique novel experimental benchmark for immunogenomics analyses in cancer research.

Yu Kong, Chris Rose, Ashley A Cass, Martine Darwish, Steve Lianoglou, Pete M Haverty, Ann-Jay Tong, Craig Blanchette, Ira Mellman, Richard Bourgon, John Greally, Suchit Jhunjhunwala, Matthew L. Albert, Haiyin Chen-Harris

Profound loss of DNA methylation is a well-recognized hallmark of cancer. Given its role in silencing transposable elements (TEs), we hypothesized that extensive TE expression occurs in tumors with highly demethylated DNA. We developed REdiscoverTE, a computational method for quantifying genome-wide TE expression in RNA sequencing data. Using The Cancer Genome Atlas database, we observed increased expression of over 400 TE subfamilies, of which 262 appeared to result from a proximal loss of DNA methylation. The most recurrent TEs were among the evolutionarily youngest in the genome, predominantly expressed from intergenic loci, and associated with antiviral or DNA damage responses. Treatment of glioblastoma cells with a demethylation agent resulted in both increased TE expression and de novo presentation of TE-derived peptides on MHC class I molecules. Therapeutic reactivation of tumor-specific TEs may synergize with immunotherapy by inducing both inflammation and the display of potentially immunogenic neoantigens.

Maxime Tarabichi, Iñigo Martincorena, Moritz Gerstung, Armand M Leroi, Florian Markowetz, Paul T. Spellman, Quaid D Morris, Ole Christian Lingjærde, David C. Wedge, Peter Van Loo, on behalf of the PCAWG Evolution and Heterogeneity Working Group

Williams et al. (Nat. Genet. 48:238-224, 2016) recently reported neutral tumor evolution in one third of 904 samples from The Cancer Genome Atlas. Here, we assess the reproducibility and validity of their method and the extent of positive selection in subclonal mutations across cancer types. Our results do not support observable neutral tumor evolution and uncover strong positive selection within subclonal mutations across cancers.

Jeremiah Wala, Ofer Shapira, Marcin Imielinski, David Craft, Steven E Schumacher

Cancer cells can acquire profound alterations to the structure of their genomes, including rearrangements that fuse distant DNA breakpoints. We analyze the distribution of somatic rearrangements across the cancer genome, using whole-genome sequencing data from 2,693 tumor-normal pairs. We observe substantial variation in the density of rearrangement breakpoints, with enrichment in open chromatin and sites with high densities of repetitive elements. After accounting for these patterns, we identify significantly recurrent breakpoints (SRBs) at 52 loci, including novel SRBs near BRD4 and AKR1C3. Taking into account both loci fused by a rearrangement, we observe different signatures resembling either single breaks followed by strand invasion or two separate breaks that become joined. Accounting for these signatures, we identify 90 pairs of loci that are significantly recurrently juxtaposed (SRJs). SRJs are primarily tumor-type specific and tend to involve genes with tissue-specific expression. SRJs were frequently associated with disruption of topology-associated domains, juxtaposition of enhancer elements, and increased expression of neighboring genes. Lastly, we find that the power to detect SRJs decreases for short rearrangements, and that reliable detection of all driver SRJs will require whole-genome sequencing data from an order of magnitude more cancer samples than currently available.

Daniele Ramazzotti, Avantika Lal, Bo Wang, Serafim Batzoglou, Arend Sidow

Outcomes for cancer patients vary greatly even within the same tumor type, and characterization of molecular subtypes of cancer holds important promise for improving prognosis and personalized treatment. This promise has motivated recent efforts to produce large amounts of multidimensional genomic ('multi-omic') data, but current algorithms still face challenges in the integrated analysis of such data. Here we present Cancer Integration via Multikernel Learning (CIMLR), a new cancer subtyping method that integrates multi-omic data to reveal molecular subtypes of cancer. We apply CIMLR to multi-omic data from 36 cancer types and show significant improvements in both computational efficiency and ability to extract biologically meaningful cancer subtypes. The discovered subtypes exhibit significant differences in patient survival for 27 of 36 cancer types. Our analysis reveals integrated patterns of gene expression, methylation, point mutations and copy number changes in multiple cancers and highlights patterns specifically associated with poor patient outcomes.

David Tamborero, Carlota Rubio-Perez, Jordi Deu-Pons, Michael P. Schroeder, Ana Vivancos, Ana Rovira, Ignasi Tusquets, Joan Albanell, Jordi Rodon, Josep Tabernero, Carmen de Torres, Rodrigo Dienstmann, Abel Gonzalez-Perez, Nuria Lopez-Bigas

While tumor genome sequencing has become widely available in clinical and research settings, the interpretation of tumor somatic variants remains an important bottleneck. Most of the alterations observed in tumors, including those in well-known cancer genes, are of uncertain significance. Moreover, the information on tumor genomic alterations shaping the response to existing therapies is fragmented across the literature and several specialized resources. Here we present the Cancer Genome Interpreter (http://www.cancergenomeinterpreter.org), an open access tool that we have implemented to annotate genomic alterations and interpret their possible role in tumorigenesis and in the response to anti-cancer therapies.

Jianfeng Shen, Wei Zhao, Zhenlin Ju, Lulu Wang, Yang Peng, Marilyne Labrie, Timothy Yap, Gordon B. Mills, Guang Peng

Poly-(ADP-ribose) polymerase (PARP) inhibitors (PARPis) have shown remarkable therapeutic efficacy against BRCA1/2 mutant cancers through a synthetic lethal interaction. PARPis are believed to exert their therapeutic effects mainly through the blockade of single-strand DNA damage repair, which leads to the accumulation of toxic DNA double strand breaks, specifically in cancer cells with DNA repair deficiency (BCRAness), including those harboring BRCA1/2 mutations. Here, we show that PARPis modulate immune reposes, which contribute to their therapeutic effects independent of BRCA1/2 mutations. The mechanism underlying this PARPi-induced reprogramming of anti-tumor microenvironment involves a promoted accumulation of cytosolic DNA fragments due to unresolved DNA lesions. This in turn activates the DNA sensing cGAS-STING pathway and stimulates production of type I interferons. Ultimately, these events promote PARPi-induced antitumor immunity independent of BRCAness, which can be further enhanced by immune checkpoint blockade. Our results may provide a mechanistic rationale for using PARPis as immunomodulatory agents to harness therapeutic efficacy of immune checkpoint blockade.