439 results about "Prostatic Cancers" patented technology

The invention provides a multi-mode prostate cancer biochemical recurrence risk layering prediction system based on artificial intelligence. Based on an Xgboost framework, a postoperative patient pathological panoramic pathological section scanning image is analyzed through end-to-end, multi-scale, multi-center and large-sample analysis, pathological information is utilized to the maximum extent, meanwhile, the prognosis risk of a patient can be evaluated more comprehensively in combination with clinical indexes such as CAPRA-S scores, and the method has obvious advantages compared with a traditional model. The method aims at better fitting the use scene of a hospital, the risk of prostate cancer recurrence of a patient is more efficiently and accurately predicted by fusing pathological section features and clinical features after a radical operation, and the risk of recurrence of the patient within 3 years and longer time after the radical operation can be accurately predicted. And an interpretable module is further combined to assist a doctor to interpret a result, so that precise layering and personalized follow-up visit of the BCR risk of the prostatic cancer patient are realized, the risk of excessive treatment and missed diagnosis is reduced, and the method has a good application prospect.

The present invention provides methods of screening for compounds that interfere with the interaction between ERG. ETV1, ETV4 or ETV5 and mSWI / SNF (BAF) chromatin remodeling complex proteins. Included are methods of screening for compounds that interfere with the interaction between ERG, ETV1, ETV4 or ETV5 and BAF155. Methods of treating prostate cancer with compounds that interfere with the interaction between ERG, ETV1, ETV4 or ETV5 and mSWI / SNF (BAF) chromatin remodeling complex proteins are also provided.

Methods for treating prostate cancer, including advanced prostate cancer, in a subject in need thereof, include administering once-daily to the subject, at least 80 mg of N-(4-(1-(2,6-difluorobenzyl)-5-((dimethylamino)methyl)-3-(6-methoxy-3-pyridazinyl)-2,4-dioxo-1,2,3,4-tetrahydrothieno[2,3-d]pyrimidin-6-yl)phenyl)-N′-methoxyurea, or a corresponding amount of a pharmaceutically acceptable salt thereof. Another method includes: administering once-daily to the subject in need thereof, an oral load dose formulation having from 240 mg to 480 mg of N-(4-(1-(2,6-difluorobenzyl)-5-((dimethylamino)methyl)-3-(6-methoxy-3-pyridazinyl)-2,4-dioxo-1,2,3,4-tetrahydrothieno[2,3-d]pyrimidin-6-yl)phenyl)-N′-methoxyurea, or a corresponding amount of a pharmaceutically acceptable salt thereof, and thereafter administering once-daily to the subject, an oral maintenance dose formulation having 80 mg to 160 mg of N-(4-(1-(2,6-difluorobenzyl)-5-((dimethylamino)methyl)-3-(6-methoxy-3-pyridazinyl)-2,4-dioxo-1,2,3,4-tetrahydrothieno[2,3-d]pyrimidin-6-yl)phenyl)-N′-methoxyurea, or a corresponding amount of a pharmaceutically acceptable salt thereof.

The invention provides a prostate cancer biochemical recurrence prediction system based on a deep learning image and clinical information multi-mode model. The prostate cancer biochemical recurrence prediction system comprises an input module, a preprocessing module, a feature selection module, a fusion module, a prediction module, a data management and storage module and a result output and decision support module. According to the method, bpMRI image data and clinical information of a patient are fully utilized, multi-mode information is fully fused through a deep learning technology, and accurate prediction of biochemical recurrence is achieved. And the robustness of the model is further enhanced by adopting 5-fold cross validation and a diversified data enhancement strategy. Experimental results show that the method can assist in making more accurate patient layering and personalized follow-up visit schemes, and potentially reduces the over-treatment and missed diagnosis risks. The invention provides an efficient and reliable AI solution for prostatic cancer prognosis evaluation, and provides powerful support for promoting precise medical treatment and making personalized treatment strategies.

The invention discloses a preparation method and application of a concave-structure SERS (Surface Enhanced Raman Scattering) substrate for prostatic cancer exosome detection, and relates to the field of Raman spectrum detection. The invention aims to solve the problems that the quantity of electromagnetic field hot spots of the currently reported SERS substrate with the concave structure is small, the SERS substrate does not have good uniformity, the size is not matched with EVs, the detected EVs signal is relatively weak, and the parting detection requirement cannot be met. The preparation method of the SERS substrate with the concave structure comprises the following steps: 1, preparing a PDMS concave trap structure array; 2, preparing a concave well array of which the surface is covered with densely arranged Au NPs; according to the invention, Au nanoparticles are grown in situ in a PDMS concave structure, a concave trap array of which the size is matched with that of EVs and the surface is covered with densely arranged Au NPs is constructed, high-sensitivity detection (lower detection limit of 105 particles / mL) and high-accuracy typing of prostatic cancer EVs are realized, and the accuracy rate can reach 98% or more.

The embodiment of the invention provides a Grignson grading method and device for a prostate cancer pathological image, equipment and a medium. The method comprises the following steps: acquiring a prostate cancer pathological image which is a full-view digital slice image; dividing a non-blank area of the prostatic cancer pathological image into a plurality of non-overlapping examples; converting the instance into a high-dimensional feature vector; the high-dimensional feature vector is input to a pre-trained multi-branch hybrid supervised network model, slice-level representation is obtained, the multi-branch hybrid supervised network model comprises an attention backbone network, instance-level branches and slice-level branches, the slice-level branches comprise slice-level benign branches and slice-level malignant branches, and slice-level representation is obtained; the slice-level malignant branch comprises an instance compression module and a component extraction module; and inputting the slice level representation into a classifier to obtain a Grignson classification result of the prostatic cancer pathological image. Based on this, the embodiment of the invention can improve the accuracy of Gleason grading of the prostatic cancer pathological image.

The invention belongs to the field of characterization learning, and particularly relates to a prostate cancer diagnosis method based on a multimodal large model prompt learning mechanism. Comprising the following steps: step 1, data preprocessing; 2, key frames and similarity are calculated, and irrelevant editing is compressed; step 3, text and image alignment training; step 4, a test stage; according to the method, a similarity-based screening mechanism is provided, ultrasonic videos under coarse-grained labels are preliminarily screened under segmentation of a large model, and focus areas are focused on in time sequence; meanwhile, a pre-processing mechanism based on a large model is provided, and the influence of a large amount of irrelevant information existing in prostate ultrasonic image scanning on the model is compressed at a data end, so that the diagnosis effect of the model is improved.

The embodiment of the invention provides a Grignson grading method and device for a prostate cancer pathological image, equipment and a medium. The method comprises the following steps: acquiring a prostate cancer pathological image which is a full-view digital slice image; performing feature extraction on the prostate cancer image to obtain pathological image features; the pathological image features are input into a multi-instance learning model based on time-frequency interaction to obtain fusion features, the multi-instance learning model based on time-frequency interaction comprises a feature reconstruction module and a time-frequency feature interaction module, the feature reconstruction module is used for extracting high-frequency feature information and low-frequency feature information from the pathological image features, and the time-frequency feature interaction module is used for interacting with the high-frequency feature information and the low-frequency feature information; the time-frequency feature interaction module is used for extracting time-domain feature information and frequency-domain feature information from pathological image features; and inputting the fusion features into a classifier to obtain a Grignson classification result of the prostatic cancer pathological image. Based on this, the embodiment of the invention can improve the accuracy of Gleason grading of the prostatic cancer pathological image.

This disclosure pertains to methods of treating prostate cancer in a subject in need thereof, including, for example, metastatic prostate cancer, castrate-resistant prostate cancer, and metastatic castrate-resistant prostate cancer, wherein the method comprises administering to the subject a therapeutically effective amount of Compound A, or a pharmaceutically acceptable salt thereof, and further comprises a step of discontinuing or reducing the administration of a CYP3A inhibitor or inducer, an efflux transporter substrate or inhibitor, or an uptake transporter substrate or inhibitor, to the subject prior to initiating administration of Compound Aor the pharmaceutically acceptable salt thereof.

The invention relates to a prostate cancer tissue Hamp based on deep learning; the invention discloses an E virtual staining algorithm, and belongs to the field of malignant tumors. The method comprises the following steps: 1, obtaining a prostate cancer tissue slice white piece and a corresponding Hamp; e, an image data set; 2, performing data preprocessing on the data set; 3, inputting the preprocessed standard data set into the model for training; step 4, inputting the data set into a designed Attention-CycleGAN network, after inputting a source domain image, outputting a conversion image of a target domain by the model, and training a prostatic cancer tissue Hamp; and E, virtualizing the dyeing model. The prostate cancer tissue Hamp based on deep learning; according to the E virtual dyeing algorithm, the model is more excellent in performance when processing a complex image conversion task, the generated image is more vivid and meticulous, the discriminator extracts the multi-scale features of the input image through the multilayer convolutional network, and the authenticity of the input image can be more accurately judged in combination with the enhancement characteristic of the space attention mechanism, so that the image conversion efficiency is improved. Therefore, the precision of the whole model is improved.

The present disclosure provides a method of detecting prostate cancer in a human subject wherein the human subject is administrated about 7 mCi to about 9 mCi of a Cu-64 PSMA I&T composition, images are obtained between 1 and 4 hours post administration, and wherein the images obtained retain acceptable image quality up to 4 hours post dose.

The invention provides a prostate cancer focus accurate positioning method based on a fusion navigation technology, and relates to the technical field of data processing, and the method comprises the steps: collecting a plurality of prostate images; selecting a control point from the prostate image in combination with a sparse regularization algorithm; determining an optimal deformation field according to the control points, and performing elastic registration on the prostate image based on the optimal deformation field; differential multi-resolution fusion is carried out on the registered prostate images, and a prostate three-dimensional model is reconstructed; constructing a lesion dynamic extraction model comprising a three-dimensional convolutional network, a pooling layer, an interested layer and a bounding box regression layer which are connected in sequence; a loss function of the focus dynamic extraction model is established, and the loss function comprises an intersection-to-union ratio loss item and a distributed focus loss item; under the supervision of the loss function, training the focus dynamic extraction model; and positioning a lesion point in the prostate three-dimensional model through the trained lesion dynamic extraction model. And the focus point positioning speed and accuracy are improved.

A real-time artificial intelligence (AI) framework is provided for the automated analysis of radiological images and detection of disease, such as extracapsular extension (ECE) in prostate cancer. The system includes a dual deep learning architecture comprising a first convolutional neural network (CNN) for identifying diagnostically relevant image slices from three-dimensional MRI data, and a second CNN for classifying disease presence based on those slices. A preprocessing pipeline standardizes and harmonizes image input, and cropping algorithms isolate the region of interest for enhanced model performance. This framework enables scalable, high-accuracy diagnosis across various imaging modalities including but not limited to MRI, CT, PET, ultrasound, and diverse disease types, improving clinical decision-making and supporting integration into real-time radiology workflows.

Methods for treating prostate cancer, including advanced prostate cancer, in a subject in need thereof, include administering once-daily to the subject, at least 80 mg of N-(4-(1-(2,6-difluorobenzyl)-5-((dimethylamino)methyl)-3-(6-methoxy-3-pyridazinyl)-2,4-dioxo-1,2,3,4-tetrahydrothieno[2,3-d]pyrimidin-6-yl)phenyl)-N′-methoxyurea, or a corresponding amount of a pharmaceutically acceptable salt thereof. Another method includes: administering once-daily to the subject in need thereof, an oral load dose formulation having from 240 mg to 480 mg of N-(4-(1-(2,6-difluorobenzyl)-5-((dimethylamino)methyl)-3-(6-methoxy-3-pyridazinyl)-2,4-dioxo-1,2,3,4-tetrahydrothieno[2,3-d]pyrimidin-6-yl)phenyl)-N′-methoxyurea, or a corresponding amount of a pharmaceutically acceptable salt thereof, and thereafter administering once-daily to the subject, an oral maintenance dose formulation having 80 mg to 160 mg of N-(4-(1-(2,6-difluorobenzyl)-5-((dimethylamino)methyl)-3-(6-methoxy-3-pyridazinyl)-2,4-dioxo-1,2,3,4-tetrahydrothieno[2,3-d]pyrimidin-6-yl)phenyl)-N′-methoxyurea, or a corresponding amount of a pharmaceutically acceptable salt thereof.

The invention relates to the technical field of pharmaceutical preparations, and particularly discloses a cyclophosphamide ternary nano-micelle and a preparation method thereof.The cyclophosphamide ternary nano-micelle prepared through the method can be used for treating malignant lymphoma, multiple myeloma, leukemia, breast cancer, ovarian cancer, cervical cancer, prostatic cancer, colon cancer, bronchial cancer, lung cancer and the like. Reasonable preparation steps and a preparation formula are adopted, the use of an organic reagent is reduced, the irritation of cyclophosphamide is reduced and the stability of the micelle is improved by adjusting a freeze-drying technology, and the cyclophosphamide micelle adopts amphiphilic macromolecules as a carrier material, so that the drug loading capacity and the stability of the micelle are obviously improved, and the bioavailability of the micelle is improved. The ternary nano-micelle system has the advantages that the solubility of cyclophosphamide is improved, the in-vivo bioavailability is increased, the preparation method is simple, the micelle performance is stable, and large-scale industrial production is facilitated.

Systems and methods for determining whether input medical images are out-of-distribution of training images on which a machine learning based medical imaging analysis network is trained are provided. One or more input medical images of a patient are received. One or more reconstructed images of the one or more input medical images are generated using a machine learning based reconstruction network. It is determined whether the one or more input medical images are out-of-distribution from training images on which a machine learning based medical imaging analysis network is trained based on the one or more input medical images and the one or more reconstructed images. The determination of whether the one or more input medical images are out-of-distribution from the training images is output.

The invention discloses application of atypical retinoid A-7 in preparation of androgen receptor inhibitors and antitumor drugs, and belongs to the technical field of medicines. It is proved for the first time that AR can be degraded through a CMA pathway, and the atypical retinoid A-7 serving as an AR inhibitor can obviously reduce the AR protein level in prostate cancer cells; in addition, the Atypic retinoids 7 can be used for inhibiting the growth and proliferation capacity of the prostate cancer cells LNCaP; in addition, the invention further provides an anti-tumor medicine composition, the anti-tumor medicine composition comprises the Atypic retinoid-7 and Enzalutamide, and the anti-tumor medicine composition can be used for treating the prostatic cancer and has important clinical significance.

The invention discloses a prostatic cancer surgical navigation method based on artificial intelligence and fluorescence-like real-time description, which comprises the following steps: S1, acquiring medical image data of a preoperative prostatic cancer patient, and preprocessing the medical image data; s2, generating dynamic fluorescence image data; s3, constructing a first layer of generative adversarial network; s4, a second generator receives the initial image output by the first generator and generates a refined dynamic tumor boundary image; s5, inputting dynamic fluorescence image data captured in real time into the trained double-layer generative adversarial network in the operation process; and S6, according to the generated refined dynamic tumor boundary image and patient data, dynamically adjusting an operation path according to the real-time change of the tumor morphology. According to the invention, the accuracy and safety of the prostatic cancer operation are obviously improved, and the dynamic tracking of tumors and the real-time adjustment of the operation path in the operation process are ensured.

The invention discloses a prostate PSMA PET / CT and enhanced MR multi-modal image registration and fusion method based on deep learning. The method comprises the following steps: preprocessing acquired PSMA PET / CT and enhanced MRI data; a multi-scale multi-stage registration network comprising the semantic gating convolution module and a U-CLSTM module is constructed: the SGC module enhances global perception ability through a V channel, the U-CLSTM module reinforces local feature attention by using a U channel, deformation field iterative optimization is performed in combination with a pyramid structure, and a multi-scale multi-stage registration network comprising the semantic gating convolution module and the U-CLSTM module is constructed; and finally, generating a high-precision registration image through Y channel weighted fusion. According to the method, global information self-adaption and local feature enhancement are combined, the registration precision of a gland anatomical structure and a tumor focus is synchronously improved through a multi-scale pyramid structure, PSMAPT / CT functional metabolism information and MRI anatomical structure information are effectively integrated, the generated three-dimensional fusion image can visually display the spatial position, morphological features and invasion range of a tumor, and the accuracy of the three-dimensional fusion image is improved. A multi-modal image basis with high space consistency is provided for clinical diagnosis, and the diagnosis accuracy of the prostate cancer is remarkably improved.

Described herein are radiopharmaceuticals for the treatment of PSMA-expressing cancers, as well as methods and / or uses of such compounds for treating PSMA-expressing cancers, such as, e.g., metastatic castration resistant prostate cancers.

The invention provides a multi-mode-based prostate cancer and hyperplasia prediction system and a construction method, relates to the technical field of medical image processing, effectively reduces errors possibly introduced by automatic division through precise combination of preliminary division and secondary identification, improves the accuracy and reliability of prostate region identification, and improves the prostate cancer and hyperplasia prediction efficiency. The time of the whole prediction process can be effectively shortened by adopting a parallel computing processing mode, the processing efficiency is improved, the accuracy of feature recognition can be further improved by matching with fusion and screening of multi-modal features, and a good guarantee is provided for high accuracy and high efficiency of prediction results of prostate cancer conditions and bone metastasis conditions of the prostate cancer conditions.

Methods for treating prostate cancer, including advanced prostate cancer, in a subject in need thereof, include administering once-daily to the subject, at least 80 mg of N-(4-(1-(2,6-difluorobenzyl)-5-((dimethylamino)methyl)-3-(6-methoxy-3-pyridazinyl)-2,4-dioxo-1,2,3,4-tetrahydrothieno[2,3-d]pyrimidin-6-yl)phenyl)-N′-methoxyurea, or a corresponding amount of a pharmaceutically acceptable salt thereof. Another method includes: administering once-daily to the subject in need thereof, an oral load dose formulation having from 240 mg to 480 mg of N-(4-(1-(2,6-difluorobenzyl)-5-((dimethylamino)methyl)-3-(6-methoxy-3-pyridazinyl)-2,4-dioxo-1,2,3,4-tetrahydrothieno[2,3-d]pyrimidin-6-yl)phenyl)-N′-methoxyurea, or a corresponding amount of a pharmaceutically acceptable salt thereof, and thereafter administering once-daily to the subject, an oral maintenance dose formulation having 80 mg to 160 mg of N-(4-(1-(2,6-difluorobenzyl)-5-((dimethylamino)methyl)-3-(6-methoxy-3-pyridazinyl)-2,4-dioxo-1,2,3,4-tetrahydrothieno[2,3-d]pyrimidin-6-yl)phenyl)-N′-methoxyurea, or a corresponding amount of a pharmaceutically acceptable salt thereof.

The invention relates to the technical field of analysis and detection of prostate cancer markers, in particular to a paper chip for simultaneously detecting multivariate prostate cancer markers as well as a preparation method and application of the paper chip. The paper chip comprises a sample injection layer, a recognition layer and a detection layer which are arranged from top to bottom, and three hydrophilic areas, namely a contrast area A, a detection area B and a detection area C, are respectively arranged on the sample injection layer, the recognition layer and the detection layer. The paper chip is mainly used for quantitatively detecting the content of two prostatic cancer markers (Sar and PSA).

This invention is in the field of medicinal chemistry. In particular, the invention relates to a new class of small-molecules having a 6,5-heterocyclic structure (e.g., compounds having a imidazopyridine, imidazopyrimidine, imidazopyrazine, imidazopyridazine, imidazotriazine, benzoimidazole, benzotriazole, benzoisoxazole, purine, indazole, triazolotriazine, triazolopyridazine, triazolopyrimidine, triazolopyrazine, triazolotetrazine, triazolopyridine, pyrazolopyrazine, pyrazolopyrimidine, pyrazolopyridazine, pyrazolotriazine, pyrazolopyridine, isoxazolopyrazine, isoxazolopyrimidine, isoxazolopyrdiazine, isoxazolotriazine, or isoxalopyridine structure) which function as inhibitors of DYRK1A, DYRK1B, and Clk-1, and their use as therapeutics for the treatment of Alzheimer's disease, Down syndrome, diabetes, glioblastoma, autoimmune diseases, cancer (e.g., glioblastoma, prostate cancer), inflammatory disorders (e.g., airway inflammation), and other diseases.

The invention discloses a new application of human YTHDF1, namely an application of a human YTHDF1 gene expression quantity detection reagent in preparation of a prostatic cancer clinical diagnosis reagent. The expression of the human YTHDF1 gene in prostatic cancer tissues and cell lines is increased; after the expression of the YTHDF1 gene is knocked down, the growth of a prostate cancer cell line, the culture of organoids and the growth of transplanted tumors in nude mice are obviously inhibited; according to the present invention, with the application of the RNF7 in the tumor cells, the proliferation of the normal prostate epithelial cells can be promoted by the overexpression, the protein level of the RNF7 in the YTHDF1 knocked-down cells is significantly inhibited, the phenotype of the YTHDF1 knocked-down cells can be rescued by the overexpression of the RNF7, and the sensitivity of the tumor cells to the chemotherapeutic drugs can be promoted by the YTHDF1 knocked-down cells so as to promote the apoptosis; the prostatic cancer can be treated by inhibiting expression drugs of the human YTHDF1 gene, inhibiting expression of the E3 ubiquitin ligase RNF7 gene or screening drugs for degrading the E3 ubiquitin ligase RNF7.

The invention discloses application of a urine extracellular vesicle protein marker combination in preparation of a prostate cancer early diagnosis product, and is characterized in that the protein marker combination comprises CD177, PEPD, DNASE1, CTNNB1, SEMA3D and SCGB1A1, the CD177, PEPD, DNASE1 and CTNNB1 protein combination is applied in preparation of the prostate cancer early diagnosis product, and the CD177, PEPD, DNASE1, SEMA3D and SCGB1A1 protein combination is applied in preparation of a gray region prostate cancer early diagnosis product. The method has the advantages that early accurate diagnosis of the prostate cancer and the gray prostate cancer is realized, the prostate cancer can be quickly and accurately diagnosed by combining with a machine learning algorithm, and the diagnosis efficiency is improved.

The invention discloses an extraction method and application of an aza-sesquiterpenoid compound which is derived from marine fungi and has anti-prostatic cancer activity. The structure of the aza-sesquiterpenoid compound is shown as a formula I-3 or pharmaceutical salt of the aza-sesquiterpenoid compound: # imgabs0 #, wherein the compound 1 is pyripyropene E, the compound 2 is pyripyropene O, and the compound 3 is 11-deacetylated pyripyropene A (11-deacetylated PPPA). The invention further discloses a preparation method of the aza-sesquiterpenoid compound with the anti-prostatic cancer activity and application of the aza-sesquiterpenoid compound with the anti-prostatic cancer activity and application of the aza-sesquiterpenoid compound with the anti-prostatic cancer activity. Molecular docking analysis shows that the compound shows anti-prostatic cancer activity, further experiments find that the compound pyripyroene O (compound 2) has remarkable cytotoxic activity on prostatic cancer cells, an alternative compound is provided for developing new prostatic cancer treatment, and the compound 2 has important significance on development of marine drug resources in China.

The invention relates to a prostatic cancer targeting CAR (chimeric antigen receptor), a recombinant expression vector containing the same, a lentivirus, CAR-T and application, and belongs to the technical field of tumor immunotherapy. In order to solve the problem that few reports are reported for treating solid tumors by CAR-T cells, the invention provides a CAR for targeting prostatic cancer, the CAR comprises an scFv fragment consisting of VH with an amino acid sequence as shown in SEQ ID NO.2, a hinge region (Gly4Ser) 3 and VL with an amino acid sequence as shown in SEQ ID NO.4, the CAR is connected to a lentiviral expression vector pLV-EF to obtain an expression vector plasmid, and the expression vector plasmid is used for expressing the prostatic cancer. The CAR-T cell is prepared by producing a high-titer lentivirus through a 293T packaging cell line, human primary T lymphocytes infected and activated by the lentivirus are used for preparing the CAR-T cell, in-vivo and in-vitro experiments prove that the CAR-T cell has a specific killing effect on prostatic cancer, and a new treatment thought and means are provided for solid tumor treatment.

The invention belongs to the technical field of in-vitro diagnostic reagents, and particularly relates to application of a detection reagent for GDF15 protein in extracellular vesicles in preparation of a prostate cancer diagnosis and / or staging kit. According to the invention, enrichment and proteomics detection are carried out on plasma EVs of patients with prostatic cancer (PCa) and benign prostatic hyperplasia (BPH), and through bioinformatics analysis, a potential EV protein marker GDF15 which can be used for early diagnosis of PCa is screened for the first time. The GDF15 protein is verified in a plurality of groups of new clinical queues by further adopting an immunological technology and a targeted quantitative mass spectrometry technology, and the distinguishing ability of the GDF15 protein to PCa and BPH and the distinguishing ability of the GDF15 protein to different PCa stages are determined. The method is expected to promote noninvasive precise diagnosis of clinical PCa, and has a good application prospect.

Provided are antibodies, fragments thereof, chimeric antigen receptors (CARs) and T cell receptors (TCRs) comprising one or more of the anti-PMCA antigen binding domains disclosed herein. SynNotch receptors that comprise an anti-PSCA binding domain Provided are polynucleotides encoding antibodies, fragments thereof, CARs, T cell receptors (TCR) and SynNotch receptors. Provided are compositions, cells and cell therapies comprising the same. Further provided are methods of treatment.