Patent Application
20230326022
This disclosure relates to machine learning identification, classification, and quantification of tertiary lymphoid structures, e.g., in tumor biopsy specimens.
Tertiary lymphoid structures (TLS) (e.g., tertiary lymphoid organ or ectopic lymphoid follicle) are ectopic lymphoid tissue composed of B-cells, T-cells, and supportive cells that develop in non-lymphoid organs and are often found in tumors. TLS support differentiation of naïve T cells to effector and memory T cells and frequently develop in areas of chronic inflammation. In the clinical pathology setting, TLS have been observed, but are not currently assessed for diagnostic pathology, or to guide therapy. Studies have shown associations between TLS and immuno-oncology (IO) treatment outcomes across multiple indications (e.g., as described in Sautes-Fridman, et al, 2019, Nat Rev Cancer 19:307and Vanhersecke, et al, “Mature tertiary lymphoid structures predict immune checkpoint inhibitor efficacy in solid tumors independently of PD-L1 expression,” Nat Cancer, 2021). Presence of TLS in various tumors shows an association with outcomes in the non-IO setting, and recently TLS have been shown to be predictive of response to IO treatment in melanoma, bone sarcoma, and RCC. See e.g., Cabrita, et al, 2020, Nature 577:561, Petitprez, et al, 2020, Nature 577:556, Helmink, et al, 2020, Nature 577:549, Bruno, N&V, 2020, Nature 577:474, Sautes-Fridman, et al, 2019, Nat Rev Cancer 19:307. In the research setting, TLS have been assessed by manual visual methods based on hematoxylin and eosin stain (H&E) and immunohistochemistry (IHC) staining. Image analysis of IHC or immunofluorescent (IF) staining has been used for quantification. These correlations are dependent on TLS maturity and localization within the tumor microenvironment (TME).
One aspect of the disclosure provides a computer-implemented method that when executed on data processing hardware causes the data processing hardware to perform operations that include receiving an input histology image for a patient diagnosed with cancer. The input histology image includes a plurality of image pixels. The operations also include processing, using a cell classification model, the input histology image to generate one or more lymphocyte density maps within the input histology image, and performing morphological image processing on the one or more lymphocyte density maps to identify one or more TLS regions within the input histology image. Each TLS region is represented by a respective cluster of lymphocyte cells. For each corresponding TLS region of the one or more TLS regions identified in the input histology image, the operations also include extracting, from the respective cluster of lymphocyte cells representing the corresponding TLS region, a respective set of TLS features, and processing, using a TLS classification model, the respective set of TLS features to classify the corresponding TLS region as one of a first TLS maturation state, a second TLS maturation state, or a third TLS maturation state.
Implementations of the disclosure may include one or more of the following optional features. In some implementations, the operations also include processing, using a tumor detection model, the input histology image to identify a tumor region within the input histology image. Here, processing the input histology image to generate the one or more lymphocyte density maps may include processing, using the cell classification model, the input histology image by performing single-cell imaging analysis on the tumor region identified within the input histology image to generate the one or more lymphocyte density maps. In these implementations, the tumor detection model may be trained by obtaining a plurality of image tiles rasterized from a set of whole-slide histopathology images, each image tile manually annotated as including a tumor or a non-tumor, and training, using a neural network, the tumor detection model on the plurality of image tiles to teach the tumor detection model to learn how to identify tumor regions within histology images.
In some examples, the cell classification model is trained by obtaining a plurality of image patches and training, using a neural network, the cell classification model on the plurality of image patches to teach the cell classification model to learn how to classify individual cells in histology images as tumor cells, lymphocyte cells, or non-malignant cells. Each image patch includes a corresponding plurality of human cells and manual annotations that label each human cell as a tumor cell, a lymphocyte cell, or a non-malignant cell.
In some implementations, the TLS classification model is trained by obtaining a training dataset comprising a plurality of training histology images, wherein each training histology image includes a tumor microenvironment and has manual annotations. The manual annotations identify one or more TLS regions in the training histology image, and for each corresponding TLS region, a ground-truth TLS maturation state indicating that the corresponding TLS region includes a first TLS maturation state, a second TLS maturation state, or a third TLS maturation state. Each TLS region is represented by a respective cluster of lymphocyte cells. In these implementations, the TLS classification model is further trained by, for each TLS region, extracting, from the respective cluster of lymphocyte cells representing the TLS region, a respective set of training TLS features, and training the TLS classification model on the respective set of training TLS features extracted for each TLS region to teach the TLS classification model to learn how to predict the ground-truth TLS grade for each corresponding TLS region. Training the TLS classification model may include training the TLS classification model using a classification and regression trees (CART) algorithm.
The first TLS maturation state may include a dense aggregate of at least a threshold number of lymphocytes that do not contain high endothelial venules or germinal centers. The second TLS maturation state may include an immature TLS including a dense aggregate of at least the threshold number of lymphocytes that contain high endothelial venules and do not contain any germinal centers. The third TLS maturation state may include a mature TLS including a dense aggregate of at least the threshold number of lymphocytes that contain high endothelial venules and germinal centers. The respective set of TLS features extracted from the respective cluster of lymphocyte cells may include an area of the corresponding TLS region, a roundness of the corresponding TLS region, and a skewness of the corresponding TLS region.
In some examples, the operations further include, for each corresponding TLS region of the one or more TLS regions identified in the input histology image, generating a respective pixel mask that highlights at least a perimeter of the corresponding TLS region, generating an output image that augments the input histology image by overlaying the respective pixel mask generated for each of the TLS regions onto the input histology image, and providing, for display on a screen in communication with the data processing hardware, the output image. In these examples, the respective pixel mask generated for each corresponding TLS region classified as the first maturation state includes a first pixel mask, the respective pixel mask generated for each corresponding TLS region classified as the second maturation state includes a second pixel mask that is visually distinguishable from the second pixel mask, and the respective pixel mask generated for each corresponding TLS region classified as the third maturation state includes a third pixel mask that is visually distinguishable from the first pixel mask and the second pixel mask.
In some implementations, the operations also include determining an overall TLS score for the input histology image based on the TLS maturation states for the one or more TLS regions identified in the histology image and the TLS features extracted from the one or more TLS regions identified in the histology image. In these implementations, the operations may also include determining a treatment recommendation to treat the patient using immunotherapy based on the overall TLS score. Here, the immunotherapy may include at least one of PD-1 inhibitor or a PD-L1 inhibitor. The operations may also include determining a predictive score of the patient's response to immunotherapy based on the TLS maturation states for the one or more TLS regions identified in the histology image and the TLS features extracted from the one or more TLS regions identified in the histology image.
Another aspect of the disclosure provides a system that includes data processing hardware and memory hardware in communication with the data processing hardware. The memory hardware stores instructions that when executed on the data processing hardware causes the data processing hardware to perform operations that include receiving an input histology image for a patient diagnosed with cancer. The input histology image includes a plurality of image pixels. The operations also include processing, using a cell classification model, the input histology image to generate one or more lymphocyte density maps within the input histology image, and performing morphological image processing on the one or more lymphocyte density maps to identify one or more TLS regions within the input histology image. Each TLS region is represented by a respective cluster of lymphocyte cells. For each corresponding TLS region of the one or more TLS regions identified in the input histology image, the operations also include extracting, from the respective cluster of lymphocyte cells representing the corresponding TLS region, a respective set of TLS features, and processing, using a TLS classification model, the respective set of TLS features to classify the corresponding TLS region as one of a first TLS maturation state, a second TLS maturation state, or a third TLS maturation state.
This aspect may include one or more of the following optional features. In some implementations, the operations also include processing, using a tumor detection model, the input histology image to identify a tumor region within the input histology image. Here, processing the input histology image to generate the one or more lymphocyte density maps may include processing, using the cell classification model, the input histology image by performing single-cell imaging analysis on the tumor region identified within the input histology image to generate the one or more lymphocyte density maps. In these implementations, the tumor detection model may be trained by obtaining a plurality of image tiles rasterized from a set of whole-slide histopathology images, each image tile manually annotated as including a tumor or a non-tumor, and training, using a neural network, the tumor detection model on the plurality of image tiles to teach the tumor detection model to learn how to identify tumor regions within histology images.
In some examples, the cell classification model is trained by obtaining a plurality of image patches and training, using a neural network, the cell classification model on the plurality of image patches to teach the cell classification model to learn how to classify individual cells in histology images as tumor cells, lymphocyte cells, or non-malignant cells. Each image patch includes a corresponding plurality of human cells and manual annotations that label each human cell as a tumor cell, a lymphocyte cell, or a non-malignant cell.
In some implementations, the TLS classification model is trained by obtaining a training dataset comprising a plurality of training histology images, wherein each training histology image includes a tumor microenvironment and has manual annotations. The manual annotations identify one or more TLS regions in the training histology image, and for each corresponding TLS region, a ground-truth TLS maturation state indicating that the corresponding TLS region includes a first TLS maturation state, a second TLS maturation state, or a third TLS maturation state. Each TLS region is represented by a respective cluster of lymphocyte cells. In these implementations, the TLS classification model is further trained by, for each TLS region, extracting, from the respective cluster of lymphocyte cells representing the TLS region, a respective set of training TLS features, and training the TLS classification model on the respective set of training TLS features extracted for each TLS region to teach the TLS classification model to learn how to predict the ground-truth TLS grade for each corresponding TLS region. Training the TLS classification model may include training the TLS classification model using a classification and regression trees (CART) algorithm.
The first TLS maturation state may include a dense aggregate of at least a threshold number of lymphocytes that do not contain high endothelial venules or germinal centers. The second TLS maturation state may include an immature TLS including a dense aggregate of at least the threshold number of lymphocytes that contain high endothelial venules and do not contain any germinal centers. The third TLS maturation state may include a mature TLS including a dense aggregate of at least the threshold number of lymphocytes that contain high endothelial venules and germinal centers. The respective set of TLS features extracted from the respective cluster of lymphocyte cells may include an area of the corresponding TLS region, a roundness of the corresponding TLS region, and a skewness of the corresponding TLS region.
In some examples, the operations further include, for each corresponding TLS region of the one or more TLS regions identified in the input histology image, generating a respective pixel mask that highlights at least a perimeter of the corresponding TLS region, generating an output image that augments the input histology image by overlaying the respective pixel mask generated for each of the TLS regions onto the input histology image, and providing, for display on a screen in communication with the data processing hardware, the output image. In these examples, the respective pixel mask generated for each corresponding TLS region classified as the first maturation state includes a first pixel mask, the respective pixel mask generated for each corresponding TLS region classified as the second maturation state includes a second pixel mask that is visually distinguishable from the second pixel mask, and the respective pixel mask generated for each corresponding TLS region classified as the third maturation state includes a third pixel mask that is visually distinguishable from the first pixel mask and the second pixel mask.
In some implementations, the operations also include determining an overall TLS score for the input histology image based on the TLS maturation states for the one or more TLS regions identified in the histology image and the TLS features extracted from the one or more TLS regions identified in the histology image. In these implementations, the operations may also include determining a treatment recommendation to treat the patient using immunotherapy based on the overall TLS score. Here, the immunotherapy may include at least one of PD-1 inhibitor or a PD-L1 inhibitor. The operations may also include determining a predictive score of the patient's response to immunotherapy based on the TLS maturation states for the one or more TLS regions identified in the histology image and the TLS features extracted from the one or more TLS regions identified in the histology image.
The details of one or more implementations of the disclosure are set forth in the accompanying drawings and the description below. Other aspects, features, and advantages will be apparent from the description and drawings, and from the claims.
Like reference symbols in the various drawings indicate like elements.
Tertiary lymphoid structures (TLSs) are ectopic lymphoid organs that develop in nonlymphoid tissues, such as sites of chronic inflammation and tumors. TLS are vascularized lymphoid structures that develop in benign and tumor tissues with chronic inflammation. TLS are highly organized structures that are similar to secondary lymphoid structures (e.g., lymph nodes). TLS can be composed of B-cell zones containing active germinal centers, surrounding T-cell zones that contain various types of dendritic cells (DCs), T-cells, high endothelial venules (HEVs), and/or other supportive cells within a structural matrix. Unlike lymph nodes, TLS lack fibrous capsules and are directly exposed to a tumor microenvironment (TME). TLS are more abundant in the invasive margin/stroma as compared to tumor cores. The presence of TLS is associated with favorable outcomes in treatment of multiple indications (e.g., treatment of melanoma with nivolumab or nivolumab and ipilimumab). TLS structures can be classified as lymphoid aggregates (LA) (i.e., a first maturation state), immature TLS (imTLS) (e.g., Grade 1) (i.e., a second maturation state), or mature TLS (mTLS) (e.g., Grade 2) with the presence of a germinal center (GC) (i.e., a third maturation state). In some cases, there is no TLS (e.g., Grade 0). While the biological mechanisms behind their formation are incompletely understood, TLSs are known to play an important role in antitumor immune response. For instance, the presence of TLSs has been associated with a favorable prognosis and improved response to immunotherapy across many cancer types.
The conventional approach to TLS detection in patients is through the technique of tissue staining for markers of immune cell lineages by multiplex immunohistochemistry or immunofluorescence techniques. However, multiplex imaging is not routinely applicable given its cost, high complexity, small field of view, and difficulty to scale, which limit its use to research settings. On the other hand, hematoxylin-eosin (H&E)-staining is widely available and remains the clinical standard in histopathology. Evaluating H&E-stained slides based on pathologist assessment is time and labor intensive, and manual and qualitative evaluations performed manually by pathologists are often inaccurate and subject to interobserver variability.
Implementations herein are directed toward leveraging machine learning techniques that use deep learning to train models to learn how to detect the presence of TLS regions in H&E-stained histology images and classify each of the TLS regions into one of three TLS maturation states. A first maturation state includes a dense aggregate of at least a threshold number of lymphocytes that do not contain high endothelial venules or germinal centers. In some examples, the threshold number is equal to 100. The second maturation state includes an immature TLS associated with a dense aggregate of at least the threshold number of lymphocytes that contain high endothelial venules and do not contain any germinal centers. The third maturation state includes a mature TLS associated with a dense aggregate of at least the threshold number of lymphocytes that contain high endothelial venules and germinal centers. More specifically, implementations include using a cell classification model to process an input histology image (e.g., H&E-stained histology image) to generate one or more lymphocyte density maps, performing morphological image processing on the one or more lymphocyte density maps to identify one or more TLS regions within the input histology image where each TLS region is represented by a respective cluster of lymphocyte cells, and for each corresponding TLS region, extracting, from the respective cluster of lymphocyte cells representing the corresponding TLS region, a respective set of TLS features. Thereafter, a trained TLS classification model receives the respective set of TLS features extracted for each corresponding TLS region to classify the corresponding TLS region as one of the first TLS maturation state, the second TLS maturation state, or the third TLS maturation state.
Implementations herein are further directed toward calculating a TLS score for the input histology image based on TLS maturation states output from TLS classification model and the TLS features for the TLS regions identified in the input histology image. A TLS scorer may determine a total area of the tumor area and also a respective TLS score for each of the three TLS maturation states that is based on the respective total TLS area of the TLS regions classified for each of the three TLS maturation states. The TLS scorer may then compute an overall TLS score for the patient associated with the input histology image based on a linear weighted sum of each respective total TLS area divided by the tumor area. Described in greater detail below, the overall TLS score may be used to predict various prognostic values for the patient such as predicting survival outcomes such as overall survival and progression-free survival. That is, higher overall TLS scores are indicative of significantly improved overall survival and progression-free survival compared to lower overall TLS scores. As such, overall TLS scores may be used to predict prognostic outcomes in lieu of using tumor stage predictions and/or prognostic outcomes predicted using tumor stage/grade may be further refined by the overall TLS scores.
For each corresponding TLS region of the one or more TLS regions identified in the input histology image, an image augmenter may generate a respective pixel mask that highlights at least a perimeter of the corresponding TLS region and then generate an output image that augments the input histology image by overlaying the respective pixel mask generated for each of the TLS regions onto the input histology image. The output image generated by the image augmenter may be provided for display on a screen for a healthcare professional (HCP) to view. Here, the image augmenter receives the classification outputs from the TLS classification model and generates visually different respective pixel masks for each of the three different TLS maturation states. For instance, the pixel mask generated for TLS regions classified as the first maturation state may include a first color, the pixel mask generated for TLS regions classified as the second maturation state may include a different second color, and the pixel mask generated for the TLS regions classified as the third maturation state may include a third color different than the first and second colors. In some examples, the pixel generated for the TLS regions classified as the third maturation state highlight at least the perimeter of the corresponding TLS region and further highlight an area of pixels encompassed by the germinal center.
Implementations herein are further directed toward a training process for training the TLS classification model. Here, the training process obtains a training dataset that includes a plurality of training histology images each containing a tumor microenvironment and including manual annotations from pathologists. The manual annotations identify the presence of TLS regions in each training histology image where each TLS region is represented by a respective cluster of lymphocyte cells. The manual annotations further identify a ground-truth TLS maturation state for each corresponding TLS region indicating that the corresponding TLS region includes the first TLS maturation state, the second TLS maturation state, or the third maturation state. Next, the training process extracts, from the respective cluster of lymphocyte cells representing each TLS region, a respective set of training TLS features that may include area of the TLS region, roundness (i.e., the ratio of the area of TLS region multiplied by 4 pi to a square of a perimeter of the TLS region), and skewness of the density of the respective cluster of lymphocyte cells representing each TLS region. Based on the respective set of training TLS features extracted for each TLS region, the training process trains the TLS model using a classification and regression trees (CART) algorithm to learn how to predict the ground-truth TLS grade for each corresponding TLS region.
Notably, the cell classification model is trained to learn how to classify individual cells in histology images as tumor cells, lymphocyte cells, or non-malignant cells. As used herein, lymphocyte cells may include T-cells and B-cells. The cell classification model may be trained using a Mask R-CNN deep learning model to learn how to segment and classify individual nuclei into tumor cells, lymphocytes, and other nonmalignant cells.
In some examples, image pre-processing is performed on the input histology image by using a tumor detection model to process the input histology image to identify a tumor region within the input histology image such that the cell classification model is used to perform single-cell image analysis on the tumor region identified within the input histology to generate the one or more lymphocyte density maps. The tumor detection model may be trained on a plurality of image tiles rasterized from a set of whole-slide histopathology images with each image tile manually annotated as including a tumor or a non-tumor. More specifically, a deep learning neural network trains the tumor detection model on the plurality of image tiles to teach the tumor detection model to learn how to identify tumor regions within histology images. The deep learning neural network may include a ResNet18 deep learning model.
Advantageously, the deep learning-based single-cell analysis techniques disclosed herein provide the ability to accurately identify, classify, and quantify the presence of TLS regions from H&E-stained whole-slide images without incurring any of the drawbacks of other techniques that adopt patch- or tile-based approaches for image analysis. Since TLSs are highly variable in size, density, and morphology, there are significant challenges using traditional patch-based approaches for identifying and interpreting TLS regions. As will become apparent, the techniques disclosed therein include quantifying the spatial distribution of lymphocytes to thereby provide an accurate and interpretable model for classification of TLSs according to their maturation states.
Similarly, manual and qualitative assessment of TLSs performed by pathologists lack automated enumeration and quantitative characterization of TLS. By the same notion, such manual and qualitative assessment of TLSs performed by pathologists are found to be inaccurate and subject to interobserver variability when assessed on H&E-stained slides. See Buisseret L, Desmedt C, Garaud S, et al. Reliability of tumor-infiltrating lymphocyte and tertiary lymphoid structure assessment in human breast cancer. Mod Pathol. 2017; 30(9):1204-1212. doi:10.1038/modpathaol.2017.43.
Referring to
The client device 111 is associated with a user 10 such as a healthcare professional (HCP), who may communicate, via a network 132, with a remote system 141. The remote system 141 may be a distributed system (e.g., cloud environment) having scalable/elastic resources 142. The resources 142 include computing resources 144 (e.g., data processing hardware) and/or storage resources 146 (e.g., memory hardware). In some implementations, the remote system 141 executes a TLS identification and quantification application 160 (also referred to as simply “application 160”) configured to execute the TLS classification model 450 in addition to other components such as a tumor detection model 450, a cell classification model 550, a lymphocyte aggregator 120, a morphological image processing module 130, a TLS extractor 145, a TLS scorer 150, and an image augmenter 360. Here, the client device 111 may access the application 160 running on the remote system 141 and input, via a graphical user interface (GUI) executing on the client device 111, the histology input image 110 to the TLS classification model 350. The GUI may be displayed to the user 10 via a screen 114 of the client device 111. The client device 111 may additionally or alternatively execute the application 160 to implement the ability to run any combination of the TLS classification model 350 and/or other components on the client device 111 for identifying, classifying, and quantifying the presence of TLS regions 135 within the histology image 110.
The TLS identification and quantification application 160 may ascertain TLS details 190 and/or a treatment recommendation 192 based on the identified TLS regions 135 classified and quantified using the TLS classification model 350. The application 160 may return the TLS details 190 and/or the treatment recommendation 192 to the client device 111 to cause the client device to display the TLS details 190 and/or the treatment recommendation 192 on the screen 114 of the client device 111. The TLS details 190 may include, without limitation, an overall TLS score 152 for the input histology image 110 as well as other details such as the number of TLS regions associated with a first maturation state (e.g., TLS1) classified by the TLS classification model 350, the number of TLS regions associated with a second maturation state (e.g., TLS2) classified by the TLS classification model 350, and the number of TLS regions associated with a third maturation state (e.g., TLS3) classified by the TLS classification model 350. Here, the first maturation state includes a dense aggregate of at least a threshold number of lymphocytes that do not contain high endothelial venules or germinal centers. In some examples, the threshold number is equal to 100. The second maturation state includes an immature TLS associated with a dense aggregate of at least the threshold number of lymphocytes that contain high endothelial venules and do not contain any germinal centers. The third maturation state includes a mature TLS associated with a dense aggregate of at least the threshold number of lymphocytes that contain high endothelial venules and germinal centers. The TLS details 190 provided for display on the screen 114 may further include an output image 110A augmenting the input histology image 110 by overlaying a respective pixel mask 112 generated for each of the TLS regions onto the input histology image 110. The treatment recommendations 192 may indicate instructions to apply (or not apply) immunotherapy to the patient for treating the patient. For instance, the immunotherapy may include a PD-1 inhibitor (e.g., an anti-PD-1 antibody) or a PD-L1 inhibitor (e.g., an anti-PID-L1 antibody). In one example, the immunotherapy includes the immune checkpoint inhibitor drug nivolumab.
The treatment recommendations 192 may further include prognostic outcomes predicted for the patient based on the TLS details 190 such as overall survival (OS) (i.e., in months), progression-free survival (PFS) (in months). The treatment recommendations 192 may show OS and/or PFS predictions for immunotherapy treatment contrasted by OS and/or PFS predictions without immunotherapy. The prognostic outcomes predicted by the application 160 may inform a patient, healthcare provider, and/or relatives of the patient for making better testing and treatment decisions for a specific health condition is diagnosed with, or for making risk-stratifications for therapeutic trials.
In some examples, the input histology image 110 undergoes initial image preprocessing to ensure sufficient image quality. The input histology image may include a 40× magnification. However, WSI slides scanned at lower magnification (e.g., 20×) may be used. To minimize the influence of image artifacts, the image preprocessing may down-sample the whole-slide images by a factor of 32 and apply appropriate color factors to remove regions with pen marks, folding, and blurring artifacts.
In the example shown, a tumor detection model 450 processes the input histology image 110 to identify one or more tumor regions 115 within the input histology image 110. Each tumor region 115 may be represented by a corresponding group of pixels where the tumor region 115 is located input histology image 110. Notably, since only TLS within or around a tumor region 115 are relevant, the tumor detection model 450 may segment cancerous tissue from normal tissue, enabling subsequent processing for TLS identification and quantification to be focused on the tumor regions 115 in the input histology image 110. The tumor detection model 450 may include a pre-trained indication-specific tissue segmentation model configured to process the input histology image 110 to distinguish cancer, cancer-associated stroma, and necrosis from normal tissue.
Referring back to
Referring back to
For each TLS region 135 identified, the application 160 executes a TLS feature extractor 145 configured to extract, from the respective cluster of lymphocyte cells representing the corresponding TLS region 135, a respective set of TLS features 140. The set of TLS features 140 may include human interpretable features (HIFs) associated with the TLS region 135. In some examples, a portion of the TLS features include sample level features including at least one of a summary count, an area, a shape, or a location of the corresponding TLS region 135. The TLS features 140 extracted from the respective cluster of lymphocyte cells representing the corresponding TLS region 135 may include an area of the TLS region 135, a roundness of the TLS region 135 (i.e., the ratio of the area of TLS region 135 multiplied by 4 pi to a square of a perimeter of the TLS region 135), and skewness of the density of the respective cluster of lymphocyte cells representing the TLS region 135). The TLS features 140 may additionally or alternatively at least one of an area of germinal center within object in tissue, an area of object in tissue, a centroid x of object in tissue, a centroid y of object in tissue, a longest distance of object from tumor, a perimeter of object in tissue, a shortest distance of object from tumor, a total germinal center within object in tissue, or an area prop germinal center within object over object in tissue. Some of the TLS features 140 may include sample level features including one or more of an area of the TLS region 135, a total count of lymphocyte cells, area proportion, count proportion, maximum area, maximum longest distance from tumor, maximum perimeter, maximum shortest distance from tumor, maximum total area, maximum total count, mean area, mean longest distance from tumor, mean perimeter, mean shortest distance from tumor, mean total area, mean total count, median area, median longest distance from tumor, median perimeter, median shortest distance from tumor, median total area, median total count, minimum area, minimum longest distance from tumor, minimum perimeter, minimum shortest distance from tumor, minimum total area, or minimum total count.
Referring back to
In some examples, the application 160 executes an image augmenter 360 configured to augment the input histology image 110 based on the TLS states 312 output from the TLS classification model 350 for the one or TLS regions 135 identified in the input histology image 110. Here, the image augmenter 360 may generate a respective pixel mask 112 that highlights at least a perimeter of each corresponding TLS region 135 based on the maturation state (e.g., TLS1, TLS2, or TLS3) of the corresponding TLS region 135. The image augmenter 360 may generate a first pixel mask 112 for TLS regions 135 classified as TLS1, a second pixel mask 112 different than the first pixel mask 112 for TLS regions 135 classified as TLS2, and a third pixel mask 112 different than the first and second pixel masks 112 for TLS regions 135 classified as TLS3. That is, different pixel masks 112 may be visually distinguishable from one another. In some examples, the different pixel masks 112 are associated with different colors. The image augmenter 360 generates an output image 110A that augments the input histology image 110 by overlaying the respective pixel mask 112 generated for each of the TLS regions 135 onto the input histology image 110. The pixel masks 112 may be overlain as graphical features that highlight at least a perimeter of each corresponding TLS region 135, thereby serving as a visual cue indicating the location and corresponding classification (e.g., TLS1, TLS2, or TLS3) of each TLS region 135 identified in the output image 110A. As will become apparent, the image augmenter 360 may apply one or more post-processing rules to generate the output image 110A. As described in the preceding paragraphs, the application 160 may provide the output image 110 as TLS details 190 to the client device 111 for display on the screen 114.
In addition to maturation states, the TLS classification model 350 and/or TLS feature extractor 145 may be further configured to output/extract topological information associated with the TLS regions 135 such as coordinates of the TLS regions 135 as well as their proximity to the tumor bed and location relative to the tumor and/or stroma a compartment. In this manner, the image augmenter 360 or an image generator may process the topological information and any combination of the input histology image, the TLS states 312, the TLS regions 135, and the TLS features to generate a topological or heat map as the output image 110A that visually depicts the topological information associated with the TLS regions 135 that may be of interest.
With continued reference to
TLS score=(w1×areaTLS1+w2×areaTLS2+w3×areaTLS3) (1)
where w1, w2, w3 are corresponding weights. The optimal corresponding weights may be selected by performing a Cox regression analysis of overall survival with each of the individual TLS areas. In one example, w1 is equal to 0.81, w2 is equal to 0.84, and w3 is equal to 1.0, suggesting that TLS regions classified as the third maturation state (e.g., mature TLS) play a most important role in antitumor immune response.
Notably, statistical analysis applied to the overall TLS score 152, as well as individual TLS scores indicated by the first, second, and third TLS areas, may be used to predict various prognostic values for the patient such as predicting survival outcomes including, but not limited to overall survival and progression-free survival. Overall survival may be defined as the time from diagnosis to death or the last follow-up. Progression-free survival may be defined as the time from diagnosis to disease progression, death, or the last follow-up. Univariate and multivariate analyses may be performed with a Cox proportional hazard model. Clinical and pathological variables, such as tumor stage and grade, may be included in the multivariate analysis. Kaplan-Meier analysis and the log-rank test may be used to evaluate patient stratification by risk group. The TLS scores may be further assessed in associated with tumor state or grade. Higher overall TLS scores are indicative of significantly improved overall survival and progression-free survival compared to lower overall TLS scores. Overall survival and progression-free survival is still better for patients with low overall TLS scores than those where no TLS regions are identified. As such, overall TLS scores may be used to predict prognostic outcomes in lieu of using tumor stage predictions and/or prognostic outcomes predicted using tumor stage/grade may be further refined by the overall TLS scores.
In some scenarios, the application 160 performs post processing to adjust the output image 110A based on any combination of the TLS features 140, the TLS score(s) 152, and the TLS states 312. In particular, the application 160 may apply the one or more post processing rules 362 to modify the pixel masks 112 by fixing small and naked germinal centers, fixing TLS regions 135 without germinal centers which were classified as the third maturation state (mature TLS), fixing mosaics to address predictions of multiple classes on a same structure due to confusion by the TLS classification model, applying object level masking to remove false positive predictions of TLS within cancer and necrosis tissue regions, and/or applying cut-offs.
Referring to
The training process 300a executes a TLS feature extraction module 320 that receives each training histology image 310 and extracts a respective set of training TLS features 140 for each TLS region 312a. That is, for each TLS region 312a annotated in the training histology image 310, the TLS feature extraction module 320 may extract, from the respective cluster of lymphocyte cells representing the TLS region 312a, the respective set of training TLS features 140. TLS feature extraction module 320 may include the pre-trained tumor extraction model 450 and the pre-trained cell classification model 550 to generate lymphocyte density maps. The feature extraction module 320 may also include any other component or combination of components executed by the application 160.
The training TLS features may include, without limitation, an area 140a of the TLS region, a roundness 140b (i.e., the ratio of the area of TLS region 312a multiplied by 4 pi to a square of a perimeter of the TLS region), and a skewness 140c of the density of the respective cluster of lymphocyte cells representing the TLS region 312a. Based on the respective set of training TLS features 140 extracted for each TLS region 312a, the training process 300a trains the TLS classification model 350 using a classification and regression trees (CART) algorithm 340 to learn how to predict the ground-truth TLS state 312b for each corresponding TLS region 312a. In some examples, the training process 300a trains the CART algorithm 340 using scikit-learn package from the Python programming language version 3.6.11 (Python Software Foundation) using default parameter settings (criterion=gini; splitter=best; min_samples_split=2). The maximum depth of trees was determined to be 4 using 5-fold cross validation in the training dataset 305. Given the relative importance of TLS3, class weights for TLS1, TLS2, and TLS3 may be empirically set to 1, 2, and 3, respectively, during training.
At operation 404, the method 400 includes processing, using a cell classification model 550, the input histology image 110 to generate one or more lymphocyte density maps 125 within the input histology image 110. At operation 406, the method 400 includes performing morphological image processing on the one or more lymphocyte density maps 125 to identify one or more TLS regions 135 within the input histology image 110. Here, each TLS region 135 is represented by a respective cluster of lymphocyte cells.
At operation 408, the method 400 includes, for each corresponding TLS region 135, extracting, from the respective cluster of lymphocyte cells representing the corresponding TLS region 135, a respective set of TLS features 140. At operation 410, the method 400 includes, for each corresponding TLS region 135, processing, using a TLS classification model 350, the respective set of TLS features to classify the corresponding TLS region as one of a first TLS maturation state, a second TLS maturation state, or a third TLS maturation state. The first TLS maturation state includes a lymphocyte aggregate of at least a threshold number of lymphocytes that do not contain high endothelial venules or germinal centers. The second TLS maturation state includes a dense aggregate of at least the threshold number of lymphocytes that contain high endothelial venules and do not contain any germinal centers. A third TLS maturation state includes a dense aggregate of at least the threshold number of lymphocytes that contain high endothelial venules and germinal centers.
Advantageously, after training the TLS classification model 350, the accuracy of the TLS classification model 350 identifying and classifying TLS regions 135 within input histology images 110 is comparable (or in some scenarios even better) than the accuracy of pathologists classifying TLSs manually. For instance, confusion matrices 500 shown in
In the example shown, a first input histology image 700, 700a corresponds to a first TLS maturation state 312, 312a indicating a lymphoid aggregate maturation state. In particular, input histology images 700 corresponding to the first TLS maturation state 312a may include a dense aggregate of at least a threshold number of lymphocytes (e.g., 100 lymphocytes) that do not contain high endothelial venules nor germinal centers. A second input histology image 700, 700b corresponds to a second TLS maturation state 312, 312b indicating an immature TLS maturation state. Input histology images 700 corresponding to the second TLS maturation state 312b may include a dense aggregate of at least the threshold number of lymphocytes (e.g., 100 lymphocytes) that contain high endothelial venules (in contrast to the first TLS maturation state 312a) but do not contain any germinal centers. A third input histology image 700, 700c corresponds to a third TLS maturation state 312, 312c indicating a mature TLS maturation state. Input histology images 700 corresponding to the third TLS maturation state 312c may include the dense aggregate of at least the threshold number of lymphocytes (e.g., 100 lymphocytes) that contain high endothelial venules and germinal centers 313 (in contrast to the first and second TLS maturation states 312a, 312b).
With continued reference to
Moreover, the image augmenter 360 generates a first pixel mask 112, 112a for each corresponding TLS region 135 classified as the first TLS maturation state 312a, a second pixel mask 112, 112b for each corresponding TLS region 135 classified as the second maturation state 312b, and a third pixel mask 112, 112c for each corresponding TLS region 135 classified as the third maturation state 312c. Notably, each pixel mask 112 is visually distinguishable from the other pixel masks 112 such that the output image 110A visually depicts the different maturation states 312 using the visually distinct pixel masks 112. As such, the output images 110A be displayed on the screen 114 of the user device 111 such that the user 10 (
For example,
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For example, as shown in
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The transcriptomic module 1710 is configured to receive, as input, the input histology images 110 and generate, as output, a gene expression signature (GES) 1712 for each respective input histology image 110. Here, the transcriptomic module 1710 may generate the GES 1712 by extracting the RNA-sequence from the respective input histology image 110. Using the TLS features 140 and the GES 1712 generated for each of the input histology images 110, the feature selector 1720 generates a feature table 1722. That is, for each respective input histology image 110, the feature extractor 1720 pairs the TLS features 140 and the GESs 172 derived from the respective input histology image 110 in the feature table 1722. The feature table 1722 includes the pairings for all of the received input histology images 110. As such, the feature table 1722 structures the TLS features 140 and the GESs 1712 such that the clustering module 1730 may determine correlations between the TLS features and the GESs 1712. In some examples, the feature table 1722 includes other TLS features 140 and the corresponding number of annotations for each TLS feature 140 in the set of input histology images as shown in table 1900 (
With continued reference to
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Anti-PD-1 antibodies that are known in the art can be used in the presently described compositions and methods. Various human monoclonal antibodies that bind specifically to PD-1 with high affinity have been disclosed in U.S. Pat. No. 8,008,449. Anti-PD-1 human antibodies disclosed in U.S. Pat. No. 8,008,449 have been demonstrated to exhibit one or more of the following characteristics: (a) bind to human PD-1 with a KD of 1×10−7 M or less, as determined by surface plasmon resonance using a Biacore biosensor system; (b) do not substantially bind to human CD28, CTLA-4 or ICOS; (c) increase T-cell proliferation in a Mixed Lymphocyte Reaction (MLR) assay; (d) increase interferon-γ production in an MLR assay; (e) increase IL-2 secretion in an MLR assay; (f) bind to human PD-1 and cynomolgus monkey PD-1; (g) inhibit the binding of PD-L1 and/or PD-L2 to PD-1; (h) stimulate antigen-specific memory responses; (i) stimulate antibody responses; and (j) inhibit tumor cell growth in vivo. Anti-PD-1 antibodies usable in the present disclosure include monoclonal antibodies that bind specifically to human PD-1 and exhibit at least one, in some embodiments, at least five, of the preceding characteristics.
Other anti-PD-1 monoclonal antibodies have been described in, for example, U.S. Pat. Nos. 6,808,710, 7,488,802, 8,168,757 and 8,354,509, US Publication No. 2016/0272708, and PCT Publication Nos. WO 2012/145493, WO 2008/156712, WO 2015/112900, WO 2012/145493, WO 2015/112800, WO 2014/206107, WO 2015/35606, WO 2015/085847, WO 2014/179664, WO 2017/020291, WO 2017/020858, WO 2016/197367, WO 2017/024515, WO 2017/025051, WO 2017/123557, WO 2016/106159, WO 2014/194302, WO 2017/040790, WO 2017/133540, WO 2017/132827, WO 2017/024465, WO 2017/025016, WO 2017/106061, WO 2017/19846, WO 2017/024465, WO 2017/025016, WO 2017/132825, and WO 2017/133540 each of which is incorporated by reference in its entirety.
In some implementations, the anti-PD-1 antibody is selected from the group consisting of nivolumab (also known as OPDIVO®, 5C4, BMS-936558, MDX-1106, and ONO-4538), pembrolizumab (Merck; also known as KEYTRUDA®, lambrolizumab, and MK-3475; see WO2008/156712), PDR001 (Novartis; see WO 2015/112900), MEDI-0680 (AstraZeneca; also known as AMP-514; see WO 2012/145493), cemiplimab (Regeneron; also known as REGN-2810; see WO 2015/112800), JS001 (TAIZHOU JUNSHI PHARMA; also known as toripalimab; see Si-Yang Liu et al., J. Hematol. Oncol. 10:136 (2017)), BGB-A317 (Beigene; also known as Tislelizumab; see WO 2015/35606 and US 2015/0079109), INCSHR1210 (Jiangsu Hengrui Medicine; also known as SHR-1210; see WO 2015/085847; Si-Yang Liu et al., J. Hematol. Oncol. 10:136 (2017)), TSR-042 (Tesaro Biopharmaceutical; also known as ANB011; see WO2014/179664), GLS-010 (Wuxi/Harbin Gloria Pharmaceuticals; also known as WBP3055; see Si-Yang Liu et al., J. Hematol. Oncol. 10:136 (2017)), AM-0001 (Armo), STI-1110 (Sorrento Therapeutics; see WO 2014/194302), AGEN2034 (Agenus; see WO 2017/040790), MGA012 (Macrogenics, see WO 2017/19846), BCD-100 (Biocad; Kaplon et al., mAbs 10(2):183-203 (2018), and IBI308 (Innovent; see WO 2017/024465, WO 2017/025016, WO 2017/132825, and WO 2017/133540).
Nivolumab is a fully human IgG4 (S228P) PD-1 immune checkpoint inhibitor antibody that selectively prevents interaction with PD-1 ligands (PD-L1 and PD-L2), thereby blocking the down-regulation of antitumor T-cell functions (U.S. Pat. No. 8,008,449; Wang et al., 2014 Cancer Immunol Res. 2(9):846-56). Pembrolizumab is a humanized monoclonal IgG4 (S228P) antibody directed against human cell surface receptor PD-1 (programmed death-1 or programmed cell death-1). Pembrolizumab is described, for example, in U.S. Pat. Nos. 8,354,509 and 8,900,587.
Anti-PD-1 antibodies usable in the disclosed compositions and methods also include isolated antibodies that bind specifically to human PD-1 and cross-compete for binding to human PD-1 with any anti-PD-1 antibody disclosed herein, e.g., nivolumab (see, e.g., U.S. Pat. Nos. 8,008,449 and 8,779,105; WO 2013/173223). In some embodiments, the anti-PD-1 antibody binds the same epitope as any of the anti-PD-1 antibodies described herein, e.g., nivolumab. The ability of antibodies to cross-compete for binding to an antigen indicates that these monoclonal antibodies bind to the same epitope region of the antigen and sterically hinder the binding of other cross-competing antibodies to that particular epitope region. These cross-competing antibodies are expected to have functional properties very similar those of the reference antibody, e.g., nivolumab, by virtue of their binding to the same epitope region of PD-1. Cross-competing antibodies can be readily identified based on their ability to cross-compete with nivolumab in standard PD-1 binding assays such as Biacore analysis, ELISA assays or flow cytometry (see, e.g., WO 2013/173223).
In some implementations, the antibodies that cross-compete for binding to human PD-1 with, or bind to the same epitope region of human PD-1 antibody, nivolumab, are monoclonal antibodies. For administration to human subjects, these cross-competing antibodies are chimeric antibodies, engineered antibodies, or humanized or human antibodies. Such chimeric, engineered, humanized or human monoclonal antibodies can be prepared and isolated by methods well known in the art.
Anti-PD-1 antibodies usable in the compositions and methods of the present disclosure also include antigen-binding portions of the above antibodies. It has been amply demonstrated that the antigen-binding function of an antibody can be performed by fragments of a full-length antibody.
Anti-PD-1 antibodies suitable for use in the disclosed compositions and methods are antibodies that bind to PD-1 with high specificity and affinity, block the binding of PD-L1 and or PD-L2, and inhibit the immunosuppressive effect of the PD-1 signaling pathway. In any of the compositions or methods disclosed herein, an anti-PD-1 “antibody” includes an antigen-binding portion or fragment that binds to the PD-1 receptor and exhibits the functional properties similar to those of whole antibodies in inhibiting ligand binding and up-regulating the immune system. In certain embodiments, the anti-PD-1 antibody or antigen-binding portion thereof cross-competes with nivolumab for binding to human PD-1.
In some examples, the anti-PD-1 antibody is administered at a dose ranging from 0.1 mg/kg to 20.0 mg/kg body weight once every 2, 3, 4, 5, 6, 7, or 8 weeks, e.g., 0.1 mg/kg to 10.0 mg/kg body weight once every 2, 3, or 4 weeks. In other embodiments, the anti-PD-1 antibody is administered at a dose of about 2 mg/kg, about 3 mg/kg, about 4 mg/kg, about 5 mg/kg, about 6 mg/kg, about 7 mg/kg, about 8 mg/kg, about 9 mg/kg, or 10 mg/kg body weight once every 2 weeks. In other embodiments, the anti-PD-1 antibody is administered at a dose of about 2 mg/kg, about 3 mg/kg, about 4 mg/kg, about 5 mg/kg, about 6 mg/kg, about 7 mg/kg, about 8 mg/kg, about 9 mg/kg, or 10 mg/kg body weight once every 3 weeks. In one embodiment, the anti-PD-1 antibody is administered at a dose of about 5 mg/kg body weight about once every 3 weeks. In another embodiment, the anti-PD-1 antibody, e.g., nivolumab, is administered at a dose of about 3 mg/kg body weight about once every 2 weeks. In other embodiments, the anti-PD-1 antibody, e.g., Pembrolizumab, is administered at a dose of about 2 mg/kg body weight about once every 3 weeks.
The anti-PD-1 antibody useful for the present disclosure can be administered as a flat dose. In some embodiments, the anti-PD-1 antibody is administered at a flat dose of from about 100 to about 1000 mg, from about 100 mg to about 900 mg, from about 100 mg to about 800 mg, from about 100 mg to about 700 mg, from about 100 mg to about 600 mg, from about 100 mg to about 500 mg, from about 200 mg to about 1000 mg, from about 200 mg to about 900 mg, from about 200 mg to about 800 mg, from about 200 mg to about 700 mg, from about 200 mg to about 600 mg, from about 200 mg to about 500 mg, from about 200 mg to about 480 mg, or from about 240 mg to about 480 mg, In one embodiment, the anti-PD-1 antibody is administered as a flat dose of at least about 200 mg, at least about 220 mg, at least about 240 mg, at least about 260 mg, at least about 280 mg, at least about 300 mg, at least about 320 mg, at least about 340 mg, at least about 360 mg, at least about 380 mg, at least about 400 mg, at least about 420 mg, at least about 440 mg, at least about 460 mg, at least about 480 mg, at least about 500 mg, at least about 520 mg, at least about 540 mg, at least about 550 mg, at least about 560 mg, at least about 580 mg, at least about 600 mg, at least about 620 mg, at least about 640 mg, at least about 660 mg, at least about 680 mg, at least about 700 mg, or at least about 720 mg at a dosing interval of about 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 weeks. In another embodiments, the anti-PD-1 antibody is administered as a flat dose of about 200 mg to about 800 mg, about 200 mg to about 700 mg, about 200 mg to about 600 mg, about 200 mg to about 500 mg, at a dosing interval of about 1, 2, 3, or 4 weeks.
In some implementations, the anti-PD-1 antibody is administered as a flat dose of about 200 mg at about once every 3 weeks. In other embodiments, the anti-PD-1 antibody is administered as a flat dose of about 200 mg at about once every 2 weeks. In other embodiments, the anti-PD-1 antibody is administered as a flat dose of about 240 mg at about once every 2 weeks. In certain embodiments, the anti-PD-1 antibody is administered as a flat dose of about 480 mg at about once every 4 weeks.
In some additional implementations, nivolumab is administered at a flat dose of about 240 mg once about every 2 weeks. In some embodiments, nivolumab is administered at a flat dose of about 240 mg once about every 3 weeks. In some embodiments, nivolumab is administered at a flat dose of about 360 mg once about every 3 weeks. In some embodiments, nivolumab is administered at a flat dose of about 480 mg once about every 4 weeks.
Alternatively, Pembrolizumab may be administered at a flat dose of about 200 mg once about every 2 weeks. In some embodiments, Pembrolizumab is administered at a flat dose of about 200 mg once about every 3 weeks. In some embodiments, Pembrolizumab is administered at a flat dose of about 400 mg once about every 4 weeks.
In some aspects, the PD-1 inhibitor is a small molecule. In some aspects, the PD-1 inhibitor includes a millamolecule. In some aspects, the PD-1 inhibitor includes a macrocyclic peptide. The PD-1 inhibitor may include BMS-986189. In some additional aspects, the PD-1 inhibitor includes an inhibitor disclosed in International Publication No. WO2014/151634, which is incorporated by reference herein in its entirety. In some aspects, the PD-1 inhibitor includes INCMGA00012 (Incyte Corporation). In some aspects, the PD-1 inhibitor includes a combination of an anti-PD-1 antibody disclosed herein and a PD-1 small molecule inhibitor.
In some implementations, an anti-PD-L1 antibody is substituted for the anti-PD-1 antibody in any of the methods disclosed herein. Anti-PD-L1 antibodies that are known in the art can be used in the compositions and methods of the present disclosure. Examples of anti-PD-L1 antibodies useful in the compositions and methods of the present disclosure include the antibodies disclosed in U.S. Pat. No. 9,580,507. Anti-PD-L1 human monoclonal antibodies disclosed in U.S. Pat. No. 9,580,507 have been demonstrated to exhibit one or more of the following characteristics: (a) bind to human PD-L1 with a KD of 1×10−7 M or less, as determined by surface plasmon resonance using a Biacore biosensor system; (b) increase T-cell proliferation in a Mixed Lymphocyte Reaction (MLR) assay; (c) increase interferon-γ production in an MLR assay; (d) increase IL-2 secretion in an MLR assay; (e) stimulate antibody responses; and (f) reverse the effect of T regulatory cells on T cell effector cells and/or dendritic cells. Anti-PD-L1 antibodies usable in the present disclosure include monoclonal antibodies that bind specifically to human PD-L1 and exhibit at least one, in some embodiments, at least five, of the preceding characteristics.
The anti-PD-L1 antibody may be selected from the group consisting of BMS-936559 (also known as 12A4, MDX-1105; see, e.g., U.S. Pat. No. 7,943,743 and WO 2013/173223), atezolizumab (Roche; also known as TECENTRIQ®; MPDL3280A, RG7446; see U.S. Pat. No. 8,217,149; see, also, Herbst et al. (2013) J Clin Oncol 31(suppl):3000), durvalumab (AstraZeneca; also known as IMFINZI™, MEDI-4736; see WO 2011/066389), avelumab (Pfizer; also known as BAVENCIO®, MSB-0010718C; see WO 2013/079174), STI-1014 (Sorrento; see WO2013/181634), CX-072 (Cytomx; see WO2016/149201), KN035 (3D Med/Alphamab; see Zhang et al., Cell Discov. 7:3 (March 2017), LY3300054 (Eli Lilly Co.; see, e.g., WO 2017/034916), BGB-A333 (BeiGene; see Desai et al., JCO 36 (15suppl):TPS3113 (2018)), and CK-301 (Checkpoint Therapeutics; see Gorelik et al., AACR:Abstract 4606 (April 2016)).
Atezolizumab is a fully humanized IgG1 monoclonal anti-PD-L1 antibody. Durvalumab is a human IgG1 kappa monoclonal anti-PD-L1 antibody. Avelumab is a human IgG1 lambda monoclonal anti-PD-L1 antibody. Anti-PD-L1 antibodies usable in the disclosed compositions and methods also include isolated antibodies that bind specifically to human PD-L1 and cross-compete for binding to human PD-L1 with any anti-PD-L1 antibody disclosed herein, e.g., atezolizumab, durvalumab, and/or avelumab. In some embodiments, the anti-PD-L1 antibody binds the same epitope as any of the anti-PD-L1 antibodies described herein, e.g., atezolizumab, durvalumab, and/or avelumab. The ability of antibodies to cross-compete for binding to an antigen indicates that these antibodies bind to the same epitope region of the antigen and sterically hinder the binding of other cross-competing antibodies to that particular epitope region. These cross-competing antibodies are expected to have functional properties very similar those of the reference antibody, e.g., atezolizumab and/or avelumab, by virtue of their binding to the same epitope region of PD-L1. Cross-competing antibodies can be readily identified based on their ability to cross-compete with atezolizumab and/or avelumab in standard PD-L1 binding assays such as Biacore analysis, ELISA assays or flow cytometry (see, e.g., WO 2013/173223).
The antibodies that cross-compete for binding to human PD-L1 with, or bind to the same epitope region of human PD-L1 antibody as, atezolizumab, durvalumab, and/or avelumab, are monoclonal antibodies. For administration to human subjects, these cross-competing antibodies are chimeric antibodies, engineered antibodies, or humanized or human antibodies. Such chimeric, engineered, humanized or human monoclonal antibodies can be prepared and isolated by methods well known in the art.
Anti-PD-L1 antibodies usable in the compositions and methods of the disclosed disclosure also include antigen-binding portions of the above antibodies. It has been amply demonstrated that the antigen-binding function of an antibody can be performed by fragments of a full-length antibody.
Anti-PD-L1 antibodies suitable for use in the disclosed compositions and methods are antibodies that bind to PD-L1 with high specificity and affinity, block the binding of PD-1, and inhibit the immunosuppressive effect of the PD-1 signaling pathway. In any of the compositions or methods disclosed herein, an anti-PD-L1 “antibody” includes an antigen-binding portion or fragment that binds to PD-L1 and exhibits the functional properties similar to those of whole antibodies in inhibiting receptor binding and up-regulating the immune system. In certain embodiments, the anti-PD-L1 antibody or antigen-binding portion thereof cross-competes with atezolizumab, durvalumab, and/or avelumab for binding to human PD-L1.
The anti-PD-L1 antibody useful for the present disclosure can be any PD-L1 antibody that specifically binds to PD-L1, e.g., antibodies that cross-compete with durvalumab, avelumab, or atezolizumab for binding to human PD-1, e.g., an antibody that binds to the same epitope as durvalumab, avelumab, or atezolizumab. In a particular embodiment, the anti-PD-L1 antibody is durvalumab. In other embodiments, the anti-PD-L1 antibody is avelumab. In some embodiments, the anti-PD-L1 antibody is atezolizumab.
In some implementations, the anti-PD-L1 antibody is administered at a dose ranging from about 0.1 mg/kg to about 20.0 mg/kg body weight, about 2 mg/kg, about 3 mg/kg, about 4 mg/kg, about 5 mg/kg, about 6 mg/kg, about 7 mg/kg, about 8 mg/kg, about 9 mg/kg, about 10 mg/kg, about 11 mg/kg, about 12 mg/kg, about 13 mg/kg, about 14 mg/kg, about 15 mg/kg, about 16 mg/kg, about 17 mg/kg, about 18 mg/kg, about 19 mg/kg, or about 20 mg/kg, about once every 2, 3, 4, 5, 6, 7, or 8 weeks.
The anti-PD-L1 antibody may be administered at a dose of about 15 mg/kg body weight at about once every 3 weeks. In other embodiments, the anti-PD-L1 antibody is administered at a dose of about 10 mg/kg body weight at about once every 2 weeks.
In some scenarios, the anti-PD-L1 antibody useful for the present disclosure is a flat dose. In some embodiments, the anti-PD-L1 antibody is administered as a flat dose of from about 200 mg to about 1600 mg, about 200 mg to about 1500 mg, about 200 mg to about 1400 mg, about 200 mg to about 1300 mg, about 200 mg to about 1200 mg, about 200 mg to about 1100 mg, about 200 mg to about 1000 mg, about 200 mg to about 900 mg, about 200 mg to about 800 mg, about 200 mg to about 700 mg, about 200 mg to about 600 mg, about 700 mg to about 1300 mg, about 800 mg to about 1200 mg, about 700 mg to about 900 mg, or about 1100 mg to about 1300 mg. In some embodiments, the anti-PD-L1 antibody is administered as a flat dose of at least about 240 mg, at least about 300 mg, at least about 320 mg, at least about 400 mg, at least about 480 mg, at least about 500 mg, at least about 560 mg, at least about 600 mg, at least about 640 mg, at least about 700 mg, at least 720 mg, at least about 800 mg, at least about 840 mg, at least about 880 mg, at least about 900 mg, at least 960 mg, at least about 1000 mg, at least about 1040 mg, at least about 1100 mg, at least about 1120 mg, at least about 1200 mg, at least about 1280 mg, at least about 1300 mg, at least about 1360 mg, or at least about 1400 mg, at a dosing interval of about 1, 2, 3, or 4 weeks. In some embodiments, the anti-PD-L1 antibody is administered as a flat dose of about 1200 mg at about once every 3 weeks. In other embodiments, the anti-PD-L1 antibody is administered as a flat dose of about 800 mg at about once every 2 weeks. In other embodiments, the anti-PD-L1 antibody is administered as a flat dose of about 840 mg at about once every 2 weeks.
Atezolizumab is administered as a flat dose of about 1200 mg once about every 3 weeks. In some examples, atezolizumab is administered as a flat dose of about 800 mg once about every 2 weeks. In other examples, atezolizumab is administered as a flat dose of about 840 mg once about every 2 weeks. Optionally, avelumab may be administered as a flat dose of about 800 mg once about every 2 weeks.
In some examples, durvalumab is administered at a dose of about 10 mg/kg once about every 2 weeks. In other examples, durvalumab is administered as a flat dose of about 800 mg/kg once about every 2 weeks. Durvalumab may optionally be administered as a flat dose of about 1200 mg/kg once about every 3 weeks.
The PD-L1 inhibitor may include a small molecule or a millamolecule. The PD-L1 inhibitor may include a macrocyclic peptide. In some implementations, the PD-L1 inhibitor includes BMS-986189. The PD-L1 inhibitor may include a millamolecule having the following formula:
where R1-R13 are amino acid side chains, Ra—Rn are hydrogen, methyl, or form a ring with a vicinal R group, and R14 is —C(O)NHR15, wherein R15 is hydrogen, or a glycine residue optionally substituted with additional glycine residues and/or tails which can improve pharmacokinetic properties. In some aspects, the PD-L1 inhibitor includes a compound disclosed in International Publication No. WO2014/151634, which is incorporated by reference herein in its entirety. In some aspects, the PD-L1 inhibitor includes a compound disclosed in International Publication No. WO2016/039749, WO2016/149351, WO2016/077518, WO2016/100285, WO2016/100608, WO2016/126646, WO2016/057624, WO2017/151830, WO2017/176608, WO2018/085750, WO2018/237153, or WO2019/070643, each of which is incorporated by reference herein in its entirety.
The PD-L1 inhibitor includes a small molecule PD-L1 inhibitor disclosed in International Publication No. WO2015/034820, WO2015/160641, WO2018/044963, WO2017/066227, WO2018/009505, WO2018/183171, WO2018/118848, WO2019/147662, or WO2019/169123, each of which is incorporated by reference herein in its entirety. In some implementations, the PD-L1 inhibitor includes a combination of an anti-PD-L1 antibody disclosed herein and a PD-L1 small molecule inhibitor disclosed herein.
A software application (i.e., a software resource) may refer to computer software that causes a computing device to perform a task. In some examples, a software application may be referred to as an “application,” an “app,” or a “program.” Example applications include, but are not limited to, system diagnostic applications, system management applications, system maintenance applications, word processing applications, spreadsheet applications, messaging applications, media streaming applications, social networking applications, and gaming applications.
The non-transitory memory may be physical devices used to store programs (e.g., sequences of instructions) or data (e.g., program state information) on a temporary or permanent basis for use by a computing device. The non-transitory memory may be volatile and/or non-volatile addressable semiconductor memory. Examples of non-volatile memory include, but are not limited to, flash memory and read-only memory (ROM)/programmable read-only memory (PROM)/erasable programmable read-only memory (EPROM)/electronically erasable programmable read-only memory (EEPROM) (e.g., typically used for firmware, such as boot programs). Examples of volatile memory include, but are not limited to, random access memory (RAM), dynamic random access memory (DRAM), static random access memory (SRAM), phase change memory (PCM) as well as disks or tapes.
The computing device 2200 includes a processor 2210, memory 2220, a storage device 2230, a high-speed interface/controller 2240 connecting to the memory 2220 and high-speed expansion ports 2250, and a low speed interface/controller 2260 connecting to a low speed bus 2270 and a storage device 2230. Each of the components 2210, 2220, 2230, 2240, 2250, and 2260, are interconnected using various busses, and may be mounted on a common motherboard or in other manners as appropriate. The processor 2210 can process instructions for execution within the computing device 2200, including instructions stored in the memory 2220 or on the storage device 2230 to display graphical information for a graphical user interface (GUI) on an external input/output device, such as display 2280 coupled to high speed interface 2240. In other implementations, multiple processors and/or multiple buses may be used, as appropriate, along with multiple memories and types of memory. Also, multiple computing devices 2200 may be connected, with each device providing portions of the necessary operations (e.g., as a server bank, a group of blade servers, or a multi-processor system).
The memory 2220 stores information non-transitorily within the computing device 2200. The memory 2220 may be a computer-readable medium, a volatile memory unit(s), or non-volatile memory unit(s). The non-transitory memory 2220 may be physical devices used to store programs (e.g., sequences of instructions) or data (e.g., program state information) on a temporary or permanent basis for use by the computing device 2200. Examples of non-volatile memory include, but are not limited to, flash memory and read-only memory (ROM)/programmable read-only memory (PROM)/erasable programmable read-only memory (EPROM)/electronically erasable programmable read-only memory (EEPROM) (e.g., typically used for firmware, such as boot programs). Examples of volatile memory include, but are not limited to, random access memory (RAM), dynamic random access memory (DRAM), static random access memory (SRAM), phase change memory (PCM) as well as disks or tapes.
The storage device 2230 is capable of providing mass storage for the computing device 2200. In some implementations, the storage device 2230 is a computer-readable medium. In various different implementations, the storage device 2230 may be a floppy disk device, a hard disk device, an optical disk device, or a tape device, a flash memory or other similar solid state memory device, or an array of devices, including devices in a storage area network or other configurations. In additional implementations, a computer program product is tangibly embodied in an information carrier. The computer program product contains instructions that, when executed, perform one or more methods, such as those described above. The information carrier is a computer- or machine-readable medium, such as the memory 2220, the storage device 2230, or memory on processor 2210.
The high speed controller 2240 manages bandwidth-intensive operations for the computing device 2200, while the low speed controller 2260 manages lower bandwidth-intensive operations. Such allocation of duties is exemplary only. In some implementations, the high-speed controller 2240 is coupled to the memory 2220, the display 2280 (e.g., through a graphics processor or accelerator), and to the high-speed expansion ports 2250, which may accept various expansion cards (not shown). In some implementations, the low-speed controller 2260 is coupled to the storage device 2230 and a low-speed expansion port 2290. The low-speed expansion port 2290, which may include various communication ports (e.g., USB, Bluetooth, Ethernet, wireless Ethernet), may be coupled to one or more input/output devices, such as a keyboard, a pointing device, a scanner, or a networking device such as a switch or router, e.g., through a network adapter.
The computing device 2200 may be implemented in a number of different forms, as shown in the figure. For example, it may be implemented as a standard server 2200a or multiple times in a group of such servers 2200a, as a laptop computer 2200b, or as part of a rack server system 2200c.
Various implementations of the systems and techniques described herein can be realized in digital electronic and/or optical circuitry, integrated circuitry, specially designed ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.
These computer programs (also known as programs, software, software applications or code) include machine instructions for a programmable processor, and can be implemented in a high-level procedural and/or object-oriented programming language, and/or in assembly/machine language. As used herein, the terms “machine-readable medium” and “computer-readable medium” refer to any computer program product, non-transitory computer readable medium, apparatus and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term “machine-readable signal” refers to any signal used to provide machine instructions and/or data to a programmable processor.
The processes and logic flows described in this specification can be performed by one or more programmable processors, also referred to as data processing hardware, executing one or more computer programs to perform functions by operating on input data and generating output. The processes and logic flows can also be performed by special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit). Processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer. Generally, a processor will receive instructions and data from a read only memory or a random access memory or both. The essential elements of a computer are a processor for performing instructions and one or more memory devices for storing instructions and data. Generally, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto optical disks, or optical disks. However, a computer need not have such devices. Computer readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media and memory devices, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto optical disks; and CD ROM and DVD-ROM disks. The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.
To provide for interaction with a user, one or more aspects of the disclosure can be implemented on a computer having a display device, e.g., a CRT (cathode ray tube), LCD (liquid crystal display) monitor, or touch screen for displaying information to the user and optionally a keyboard and a pointing device, e.g., a mouse or a trackball, by which the user can provide input to the computer. Other kinds of devices can be used to provide interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback; and input from the user can be received in any form, including acoustic, speech, or tactile input. In addition, a computer can interact with a user by sending documents to and receiving documents from a device that is used by the user; for example, by sending web pages to a web browser on a user's client device in response to requests received from the web browser.
A number of implementations have been described. Nevertheless, it will be understood that various modifications may be made without departing from the spirit and scope of the disclosure. Accordingly, other implementations are within the scope of the following claims.
This U.S. patent application claims priority under 35 U.S.C. § 119(e) to U.S. Provisional Application 63/329,352, filed on Apr. 8, 2022, and U.S. Provisional Application 63/422,763, filed on Nov. 4, 2022. The disclosures of these prior applications are considered part of the disclosure of this application and are hereby incorporated by reference in their entireties.
| Number | Date | Country | |
|---|---|---|---|
| 63422763 | Nov 2022 | US | |
| 63329352 | Apr 2022 | US |
