NK cells with tissue-resident traits shape response to immunotherapy by inducing adaptive antitumor immunity

T cell-directed cancer immunotherapy often fails to generate lasting tumor control. Harnessing additional effectors of the immune response against tumors may strengthen the clinical benefit of immunotherapies. Here, we demonstrate that therapeutic targeting of the interferon-γ (IFN-γ)-interleukin-12 (IL-12) pathway relies on the ability of a population of natural killer (NK) cells with tissue-resident traits to orchestrate an antitumor microenvironment. In particular, we used an engineered adenoviral platform as a tool for intratumoral IL-12 immunotherapy (AdV5-IL-12) to generate adaptive antitumor immunity. Mechanistically, we demonstrate that AdV5-IL-12 is capable of inducing the expression of CC-chemokine ligand 5 (CCL5) in CD49a+ NK cells both in tumor mouse models and tumor specimens from patients with cancer. AdV5-IL-12 imposed CCL5-induced type I conventional dendritic cell (cDC1) infiltration and thus increased DC-CD8 T cell interactions. A similar observation was made for other IFN-γ-inducing therapies such as Programmed cell death 1 (PD-1) blockade. Conversely, failure to respond to IL-12 and PD-1 blockade in tumor models with low CD49a+ CXCR6+ NK cell infiltration could be overcome by intratumoral delivery of CCL5. Thus, therapeutic efficacy depends on the abundance of NK cells with tissue-resident traits and, specifically, their capacity to produce the DC chemoattractant CCL5. Our findings reveal a barrier for T cell-focused therapies and offer mechanistic insights into how T cell-NK cell-DC cross-talk can be enhanced to promote antitumor immunity and overcome resistance

NK cells with tissue-resident traits shape response to immunotherapy by inducing adaptive antitumor immunity

T cell-directed cancer immunotherapy often fails to generate lasting tumor control. Harnessing additional effectors of the immune response against tumors may strengthen the clinical benefit of immunotherapies. Here, we demonstrate that therapeutic targeting of the interferon-γ (IFN-γ)-interleukin-12 (IL-12) pathway relies on the ability of a population of natural killer (NK) cells with tissue-resident traits to orchestrate an antitumor microenvironment. In particular, we used an engineered adenoviral platform as a tool for intratumoral IL-12 immunotherapy (AdV5-IL-12) to generate adaptive antitumor immunity. Mechanistically, we demonstrate that AdV5-IL-12 is capable of inducing the expression of CC-chemokine ligand 5 (CCL5) in CD49a; +; NK cells both in tumor mouse models and tumor specimens from patients with cancer. AdV5-IL-12 imposed CCL5-induced type I conventional dendritic cell (cDC1) infiltration and thus increased DC-CD8 T cell interactions. A similar observation was made for other IFN-γ-inducing therapies such as Programmed cell death 1 (PD-1) blockade. Conversely, failure to respond to IL-12 and PD-1 blockade in tumor models with low CD49a; +; CXCR6; +; NK cell infiltration could be overcome by intratumoral delivery of CCL5. Thus, therapeutic efficacy depends on the abundance of NK cells with tissue-resident traits and, specifically, their capacity to produce the DC chemoattractant CCL5. Our findings reveal a barrier for T cell-focused therapies and offer mechanistic insights into how T cell-NK cell-DC cross-talk can be enhanced to promote antitumor immunity and overcome resistance

RNA-Seq Mapping and Detection of Gene Fusions with a Suffix Array Algorithm

High-throughput RNA sequencing enables quantification of transcripts (both known and novel), exon/exon junctions and fusions of exons from different genes. Discovery of gene fusions–particularly those expressed with low abundance– is a challenge with short- and medium-length sequencing reads. To address this challenge, we implemented an RNA-Seq mapping pipeline within the LifeScope software. We introduced new features including filter and junction mapping, annotation-aided pairing rescue and accurate mapping quality values. We combined this pipeline with a Suffix Array Spliced Read (SASR) aligner to detect chimeric transcripts. Performing paired-end RNA-Seq of the breast cancer cell line MCF-7 using the SOLiD system, we called 40 gene fusions among over 120,000 splicing junctions. We validated 36 of these 40 fusions with TaqMan assays, of which 25 were expressed in MCF-7 but not the Human Brain Reference. An intra-chromosomal gene fusion involving the estrogen receptor alpha gene ESR1, and another involving the RPS6KB1 (Ribosomal protein S6 kinase beta-1) were recurrently expressed in a number of breast tumor cell lines and a clinical tumor sample

Algorithmic aspects of constrained unit disk graphs

Computational problems on graphs often arise in two- or three-dimensional geometric contexts. Such problems include assigning channels to radio transmitters (graph colouring), physically routing traces on a printed circuit board (graph drawing), and modelling molecules. It is reasonable to expect that natural graph problems have more efficient solutions when restricted to such geometric graphs. Unfortunately, many familiar NPcomplete problems remain NP-complete on geometric graphs. Indifference graphs arise in a one-dimensional geometric context; they are the intersection graphs of unit intervals on the line. Many NP-complete problems on arbitrary graphs do have efficient solutions on indifference graphs. Yet these same problems remain NP-complete for the intersection graphs of unit disks in the plane (unit disk graphs), a natural two-dimensional generalization of indifference graphs. What accounts for this situation, and how can algorithms be designed to deal with it? To study these issues, this thesis identifies a range of subclasses of unit disk graphs in which the second spatial dimension is gradually, introduced. More specifically, τ-strip graphs "interpolate" between unit disk graphs and indifference graphs; they are the intersection graphs of unit-diameter disks whose centres are constrained to lie in a strip of thickness τ. This thesis studies algorithmic and structural aspects of varying the value τ for τ-strip graphs. The thesis takes significant steps towards characterizing, recognizing, and laying out strip graphs. We will also see how to develop algorithms for several problems on strip graphs, and how to exploit their geometric representation. In particular, we will see that problems become especially tractable when the strips are "thin" (τ is small) or "discrete" (the number of possible y-coordinates for the disks is small). Note again that indifference graphs are the thinnest (τ = 0) and most discrete (one y-coordinate) of the nontrivial τ-strip graphs. The immediate results of this research concern algorithms for a specific class of graphs. The real contribution of this research is the elucidation of when and where geometry can be exploited in the development of efficient graph theoretic algorithms.Science, Faculty ofComputer Science, Department ofGraduat

Slant from texture : computational methods for recovering surface slant from images of textured scenes

This thesis examines the problem of computationally recovering or determining the slant of a surface from an image of that surface. Images are restricted to those of planar surfaces produced by orthographic projection. This thesis is concerned only with those cues obtainable from the image texture. These cues arise primarily due to the foreshortening property of orthographic projection. Texture measures have typically been partitioned into three classes: statistical approaches, micro-structural approaches, and macro-structural approaches. In this thesis, measures from each of these classes are used to develop algorithms capable of detecting surface orientation. It is concluded that these three classes are not distinct and, indeed, are artificially rendered by the prevailing definition of texture. A new definition involving nested structures is suggested.Science, Faculty ofComputer Science, Department ofGraduat

On the Complexity of Recognizing Intersection and Touching Graphs of Disks

Disk intersection (respectively, touching) graphs are the inersection graphs of closed disks in the plane whose interiors may (respectively, may not) overlap. In a previous paper [BK93], we showed that the recognition problem for unit disk intersection graphs (i.e. intersection graphs of unit disks) is NP-hard. That proof is easily modified to apply to unit disk touching graphs as well. In this paper, we show how to generalize our earlier construction to accomodate disks whose size may differ. In particular, we prove that the recognition problems for both bounded-ratio disk intersection graphs and bounded-ratio disk touching graphs are also NP-hard. (By bounded-ratio we refer to the natural generalization of the unit constraint in which the radius ratio of the largest to smallest permissible disk is bounded by some fixed constant.) The latter result contrasts with the fact that the disk touching graphs (of unconstrained ratio) are precisely the planar graphs, and are hence polynomial time recognizable. The recognition problem for disk intersection graphs (of unconstrained ratio) has recently been shown to be NP-hard as well [Kra95]

Linear Time Euclidean Distance Transform Algorithms

Two linear time (and hence asymptotically optimal) algorithms for computing the Euclidean distance transform of a two-dimensional binary image are presented. The algorithms are based on the construction and regular sampling of the Voronoi diagram whose sites consist of the unit (feature) pixels in the image. The first algorithm, which is of primarily theoretical interest, constructs the complete Voronoi diagram. The second, more practical, algorithm constructs the Voronoi diagram where it intersects the horizontal lines passing through the image pixel centres. Extensions to higher dimensional images and to other distance functions are also discussed. 1 Introduction A two-dimensional binary image is a function, I, from the elements of an n by m array, referred to as pixels, to f0; 1g. Pixels of unit (respectively, zero) value are referred to as feature (respectively, background) pixels of the image. We associate the pixel in row r and column c with the Cartesian point (c; r). Thus, an..