The 3D Revolution in Cancer Discovery.

SUMMARY: The transition from 2D to 3D spatial profiling marks a revolutionary era in cancer research, offering unprecedented potential to enhance cancer diagnosis and treatment. This commentary outlines the experimental and computational advancements and challenges in 3D spatial molecular profiling, underscoring the innovation needed in imaging tools, software, artificial intelligence, and machine learning to overcome implementation hurdles and harness the full potential of 3D analysis in the field.

ACTA2 Expression Predicts Survival and Is Associated with Response to Immune Checkpoint Inhibitors in Gastric Cancer.

PURPOSE: We sought to identify biomarkers that predict overall survival (OS) and response to immune checkpoint inhibitors (ICI) for patients with gastric cancer. EXPERIMENTAL DESIGN: This was a retrospective study of multiple independent cohorts of patients with gastric cancer. The association between tumor ACTA2 expression and OS and ICI response were determined in patients whose tumors were analyzed with bulk mRNA sequencing. Single-cell RNA sequencing (scRNA-seq) and digital spatial profiling data were used to compare tumors from patients with gastric cancer who did and did not respond to ICI. RESULTS: Increasing tumor ACTA2 expression was independently associated with worse OS in a 567-patient discovery cohort [HR, 1.28 per unit increase; 95% confidence interval (CI), 1.02-1.62]. This finding was validated in three independent cohorts (n = 974; HR, 1.52 per unit increase; 95% CI, 1.34-1.73). Of the 108 patients treated with ICI, 56% of patients with low ACTA2 expression responded to ICI versus 25% of patients with high ACTA2 expression (P = 0.004). In an analysis of a publicly available scRNA-seq dataset of 5 microsatellite instability-high patients treated with ICI, the patient who responded to ICI had lower tumor stromal ACTA2 expression than the 4 nonresponders. Digital spatial profiling of tumor samples from 4 ICI responders and 5 ICI nonresponders revealed that responders may have lower ACTA2 expression in α-SMA-positive cancer-associated fibroblasts (CAF) than nonresponders (median: 5.00 vs. 5.50). CONCLUSIONS: ACTA2 expression is associated with survival and ICI response in patients with gastric cancer. ACTA2 expression in CAFs, but not in other cellular compartments, appears to be associated with ICI response.

Spatial heterogeneity and organization of tumor mutation burden with immune infiltrates within tumors based on whole slide images correlated with patient survival in bladder cancer.

Background: High tumor mutation burden (TMB-H) could result in an increased number of neoepitopes from somatic mutations expressed by a patient's own tumor cell which can be recognized and targeted by neighboring tumor-infiltrating lymphocytes (TILs). Deeper understanding of spatial heterogeneity and organization of tumor cells and their neighboring immune infiltrates within tumors could provide new insights into tumor progression and treatment response. Methods: Here we first developed computational approaches using whole slide images (WSIs) to predict bladder cancer patients' TMB status and TILs across tumor regions, and then investigate spatial heterogeneity and organization of regions harboring TMB-H tumor cells and TILs within tumors, as well as their prognostic utility. Results: In experiments using WSIs from The Cancer Genome Atlas (TCGA) bladder cancer (BLCA), our findings show that computational pathology can reliably predict patient-level TMB status and delineate spatial TMB heterogeneity and co-organization with TILs. TMB-H patients with low spatial heterogeneity enriched with high TILs show improved overall survival. Conclusions: Computational approaches using WSIs have the potential to provide rapid and cost-effective TMB testing and TILs detection. Survival analysis illuminates potential clinical utility of spatial heterogeneity and co-organization of TMB and TILs as a prognostic biomarker in BLCA which warrants further validation in future studies.

Sequential Single-Cell Transcriptional and Protein Marker Profiling Reveals TIGIT as a Marker of CD19 CAR-T Cell Dysfunction in Patients with Non-Hodgkin Lymphoma.

Chimeric antigen receptor T-cell (CAR-T cell) therapy directed at CD19 produces durable remissions in the treatment of relapsed/refractory non-Hodgkin lymphoma (NHL). Nonetheless, many patients receiving CD19 CAR-T cells fail to respond for unknown reasons. To reveal changes in 4-1BB-based CD19 CAR-T cells and identify biomarkers of response, we used single-cell RNA sequencing and protein surface marker profiling of patient CAR-T cells pre- and postinfusion into patients with NHL. At the transcriptional and protein levels, we note the evolution of CAR-T cells toward a nonproliferative, highly differentiated, and exhausted state, with an enriched exhaustion profile in CAR-T cells of patients with poor response marked by TIGIT expression. Utilizing in vitro and in vivo studies, we demonstrate that TIGIT blockade alone improves the antitumor function of CAR-T cells. Altogether, we provide evidence of CAR-T cell dysfunction marked by TIGIT expression driving a poor response in patients with NHL. SIGNIFICANCE: This is the first study investigating the mechanisms linked to CAR-T patient responses based on the sequential analysis of manufactured and infused CAR-T cells using single-cell RNA and protein expression data. Furthermore, our findings are the first to demonstrate an improvement of CAR-T cell efficacy with TIGIT inhibition alone. This article is highlighted in the In This Issue feature, p. 1825.

Spatial analysis of tumor-infiltrating lymphocytes in histological sections using deep learning techniques predicts survival in colorectal carcinoma.

This study aimed to explore the prognostic impact of spatial distribution of tumor-infiltrating lymphocytes (TILs) quantified by deep learning (DL) approaches based on digitalized whole-slide images stained with hematoxylin and eosin in patients with colorectal cancer (CRC). The prognostic impact of spatial distributions of TILs in patients with CRC was explored in the Yonsei cohort (n = 180) and validated in The Cancer Genome Atlas (TCGA) cohort (n = 268). Two experienced pathologists manually measured TILs at the most invasive margin (IM) as 0-3 by the Klintrup-Mäkinen (KM) grading method and this was compared to DL approaches. Inter-rater agreement for TILs was measured using Cohen's kappa coefficient. On multivariate analysis of spatial TIL features derived by DL approaches and clinicopathological variables including tumor stage, microsatellite instability, and KRAS mutation, TIL densities within 200 µm of the IM (f_im200) remained the most significant prognostic factor for progression-free survival (PFS) (hazard ratio [HR] 0.004 [95% confidence interval, CI, 0.0001-0.15], p = 0.0028) in the Yonsei cohort. On multivariate analysis using the TCGA dataset, f_im200 retained prognostic significance for PFS (HR 0.031 [95% CI 0.001-0.645], p = 0.024). Inter-rater agreement of manual KM grading was insignificant in the Yonsei (κ = 0.109) and the TCGA (κ = 0.121) cohorts. The survival analysis based on KM grading showed statistically significant different PFS in the TCGA cohort, but not the Yonsei cohort. Automatic quantification of TILs at the IM based on DL approaches shows prognostic utility to predict PFS, and could provide robust and reproducible TIL density measurement in patients with CRC.

Multi-omics reveals microbiome, host gene expression, and immune landscape in gastric carcinogenesis.

To date, there has been no multi-omic analysis characterizing the intricate relationships between the intragastric microbiome and gastric mucosal gene expression in gastric carcinogenesis. Using multi-omic approaches, we provide a comprehensive view of the connections between the microbiome and host gene expression in distinct stages of gastric carcinogenesis (i.e., healthy, gastritis, cancer). Our integrative analysis uncovers various associations specific to disease states. For example, uniquely in gastritis, Helicobacteraceae is highly correlated with the expression of FAM3D, which has been previously implicated in gastrointestinal inflammation. In addition, in gastric cancer but not in adjacent gastritis, Lachnospiraceae is highly correlated with the expression of UBD, which regulates mitosis and cell cycle time. Furthermore, lower abundances of B cell signatures in gastric cancer compared to gastritis may suggest a previously unidentified immune evasion process in gastric carcinogenesis. Our study provides the most comprehensive description of microbial, host transcriptomic, and immune cell factors of the gastric carcinogenesis pathway.

Lenvatinib inhibits the growth of gastric cancer patient-derived xenografts generated from a heterogeneous population.

BACKGROUND: Lenvatinib is a multitargeted tyrosine kinase inhibitor that is being tested in combination with immune checkpoint inhibitors to treat advanced gastric cancer; however, little data exists regarding the efficacy of lenvatinib monotherapy. Patient-derived xenografts (PDX) are established by engrafting human tumors into immunodeficient mice. The generation of PDXs may be hampered by growth of lymphomas. In this study, we compared the use of mice with different degrees of immunodeficiency to establish PDXs from a diverse cohort of Western gastric cancer patients. We then tested the efficacy of lenvatinib in this system. METHODS: PDXs were established by implanting gastric cancer tissue into NOD.Cg-PrkdcscidIl2rgtm1Wjl/SzJ (NSG) or Foxn1nu (nude) mice. Tumors from multiple passages from each PDX line were compared histologically and transcriptomically. PDX-bearing mice were randomized to receive the drug delivery vehicle or lenvatinib. After 21 days, the percent tumor volume change (%Δvtumor) was calculated. RESULTS: 23 PDX models were established from Black, non-Hispanic White, Hispanic, and Asian gastric cancer patients. The engraftment rate was 17% (23/139). Tumors implanted into NSG (16%; 18/115) and nude (21%; 5/24) mice had a similar engraftment rate. The rate of lymphoma formation in nude mice (0%; 0/24) was lower than in NSG mice (20%; 23/115; p < 0.05). PDXs derived using both strains maintained histologic and gene expression profiles across passages. Lenvatinib treatment (mean %Δvtumor: -33%) significantly reduced tumor growth as compared to vehicle treatment (mean %Δvtumor: 190%; p < 0.0001). CONCLUSIONS: Nude mice are a superior platform than NSG mice for generating PDXs from gastric cancer patients. Lenvatinib showed promising antitumor activity in PDXs established from a diverse Western patient population and warrants further investigation in gastric cancer.

Development and validation of a prognostic and predictive 32-gene signature for gastric cancer.

Genomic profiling can provide prognostic and predictive information to guide clinical care. Biomarkers that reliably predict patient response to chemotherapy and immune checkpoint inhibition in gastric cancer are lacking. In this retrospective analysis, we use our machine learning algorithm NTriPath to identify a gastric-cancer specific 32-gene signature. Using unsupervised clustering on expression levels of these 32 genes in tumors from 567 patients, we identify four molecular subtypes that are prognostic for survival. We then built a support vector machine with linear kernel to generate a risk score that is prognostic for five-year overall survival and validate the risk score using three independent datasets. We also find that the molecular subtypes predict response to adjuvant 5-fluorouracil and platinum therapy after gastrectomy and to immune checkpoint inhibitors in patients with metastatic or recurrent disease. In sum, we show that the 32-gene signature is a promising prognostic and predictive biomarker to guide the clinical care of gastric cancer patients and should be validated using large patient cohorts in a prospective manner.

Machine Learning and Artificial Intelligence-driven Spatial Analysis of the Tumor Immune Microenvironment in Pathology Slides.

A better understanding of the tumor immune microenvironment (TIME) could lead to accurate diagnosis, prognosis, and treatment stratification. Although molecular analyses at the tissue and/or single cell level could reveal the cellular status of the tumor microenvironment, these approaches lack information related to spatial-level cellular distribution, co-organization, and cell-cell interaction in the TIME. With the emergence of computational pathology coupled with machine learning (ML) and artificial intelligence (AI), ML- and AI-driven spatial TIME analyses of pathology images could revolutionize our understanding of the highly heterogeneous and complex molecular architecture of the TIME. In this review we highlight recent studies on spatial TIME analysis of pathology slides using state-of-the-art ML and AI algorithms. PATIENT SUMMARY: This mini-review reports recent advances in machine learning and artificial intelligence for spatial analysis of the tumor immune microenvironment in pathology slides. This information can help in understanding the spatial heterogeneity and organization of cells in patient tumors.

Single cell RNA sequencing of AML initiating cells reveals RNA-based evolution during disease progression.

The prognosis of most patients with AML is poor due to frequent disease relapse. The cause of relapse is thought to be from the persistence of leukemia initiating cells (LIC's) following treatment. Here we assessed RNA based changes in LICs from matched patient diagnosis and relapse samples using single-cell RNA sequencing. Previous studies on AML progression have focused on genetic changes at the DNA mutation level mostly in bulk AML cells and demonstrated the existence of DNA clonal evolution. Here we identified in LICs that the phenomenon of RNA clonal evolution occurs during AML progression. Despite the presence of vast transcriptional heterogeneity at the single cell level, pathway analysis identified common signaling networks involving metabolism, apoptosis and chemokine signaling that evolved during AML progression and become a signature of relapse samples. A subset of this gene signature was validated at the protein level in LICs by flow cytometry from an independent AML cohort and functional studies were performed to demonstrate co-targeting BCL2 and CXCR4 signaling may help overcome therapeutic challenges with AML heterogeneity. It is hoped this work will facilitate a greater understanding of AML relapse leading to improved prognostic biomarkers and therapeutic strategies to target LIC's.

Associations between lipids in selected brain regions, plasma miRNA, and behavioral and cognitive measures following 28Si ion irradiation.

The space radiation environment consists of multiple species of charged particles, including 28Si ions, that may impact brain function during and following missions. To develop biomarkers of the space radiation response, BALB/c and C3H female and male mice and their F2 hybrid progeny were irradiated with 28Si ions (350 MeV/n, 0.2 Gy) and tested for behavioral and cognitive performance 1, 6, and 12 months following irradiation. The plasma of the mice was collected for analysis of miRNA levels. Select pertinent brain regions were dissected for lipidomic analyses and analyses of levels of select biomarkers shown to be sensitive to effects of space radiation in previous studies. There were associations between lipids in select brain regions, plasma miRNA, and cognitive measures and behavioral following 28Si ion irradiation. Different but overlapping sets of miRNAs in plasma were found to be associated with cognitive measures and behavioral in sham and irradiated mice at the three time points. The radiation condition revealed pathways involved in neurodegenerative conditions and cancers. Levels of the dendritic marker MAP2 in the cortex were higher in irradiated than sham-irradiated mice at middle age, which might be part of a compensatory response. Relationships were also revealed with CD68 in miRNAs in an anatomical distinct fashion, suggesting that distinct miRNAs modulate neuroinflammation in different brain regions. The associations between lipids in selected brain regions, plasma miRNA, and behavioral and cognitive measures following 28Si ion irradiation could be used for the development of biomarker of the space radiation response.

Radiomics Features of 18F-Fluorodeoxyglucose Positron-Emission Tomography as a Novel Prognostic Signature in Colorectal Cancer.

The aim of this study was to investigate the prognostic value of radiomics signatures derived from 18F-fluorodeoxyglucose (18F-FDG) positron-emission tomography (PET) in patients with colorectal cancer (CRC). From April 2008 to Jan 2014, we identified CRC patients who underwent 18F-FDG-PET before starting any neoadjuvant treatments and surgery. Radiomics features were extracted from the primary lesions identified on 18F-FDG-PET. Patients were divided into a training and validation set by random sampling. A least absolute shrinkage and selection operator Cox regression model was applied for prognostic signature building with progression-free survival (PFS) using the training set. Using the calculated radiomics score, a nomogram was developed, and its clinical utility was assessed in the validation set. A total of 381 patients with surgically resected CRC patients (training set: 228 vs. validation set: 153) were included. In the training set, a radiomics signature labeled as a rad_score was generated using two PET-derived features, such as gray-level run length matrix long-run emphasis (GLRLM_LRE) and gray-level zone length matrix short-zone low-gray-level emphasis (GLZLM_SZLGE). Patients with a high rad_score in the training and validation set had a shorter PFS. Multivariable analysis revealed that the rad_score was an independent prognostic factor in both training and validation sets. A radiomics nomogram, developed using rad_score, nodal stage, and lymphovascular invasion, showed good performance in the calibration curve and comparable predictive power with the staging system in the validation set. Textural features derived from 18F-FDG-PET images may enable detailed stratification of prognosis in patients with CRC.

Myeloid-Derived Suppressor Cell Subsets Drive Glioblastoma Growth in a Sex-Specific Manner.

Myeloid-derived suppressor cells (MDSC) that block antitumor immunity are elevated in glioblastoma (GBM) patient blood and tumors. However, the distinct contributions of monocytic (mMDSC) versus granulocytic (gMDSC) subsets have yet to be determined. In mouse models of GBM, we observed that mMDSCs were enriched in the male tumors, whereas gMDSCs were elevated in the blood of females. Depletion of gMDSCs extended survival only in female mice. Using gene-expression signatures coupled with network medicine analysis, we demonstrated in preclinical models that mMDSCs could be targeted with antiproliferative agents in males, whereas gMDSC function could be inhibited by IL1ß blockade in females. Analysis of patient data confirmed that proliferating mMDSCs were predominant in male tumors and that a high gMDSC/IL1ß gene signature correlated with poor prognosis in female patients. These findings demonstrate that MDSC subsets differentially drive immune suppression in a sex-specific manner and can be leveraged for therapeutic intervention in GBM. SIGNIFICANCE: Sexual dimorphism at the level of MDSC subset prevalence, localization, and gene-expression profile constitutes a therapeutic opportunity. Our results indicate that chemotherapy can be used to target mMDSCs in males, whereas IL1 pathway inhibitors can provide benefit to females via inhibition of gMDSCs.See related commentary by Gabrilovich et al., p. 1100.This article is highlighted in the In This Issue feature, p. 1079.

Hispanic/Latino Patients with Gastric Adenocarcinoma Have Distinct Molecular Profiles Including a High Rate of Germline CDH1 Variants.

Hispanic/Latino patients have a higher incidence of gastric cancer and worse cancer-related outcomes compared with patients of other backgrounds. Whether there is a molecular basis for these disparities is unknown, as very few Hispanic/Latino patients have been included in previous studies. To determine the genomic landscape of gastric cancer in Hispanic/Latino patients, we performed whole-exome sequencing (WES) and RNA sequencing on tumor samples from 57 patients; germline analysis was conducted on 83 patients. The results were compared with data from Asian and White patients published by The Cancer Genome Atlas. Hispanic/Latino patients had a significantly larger proportion of genomically stable subtype tumors compared with Asian and White patients (65% vs. 21% vs. 20%, P < 0.001). Transcriptomic analysis identified molecular signatures that were prognostic. Of the 43 Hispanic/Latino patients with diffuse-type cancer, 7 (16%) had germline variants in CDH1. Variant carriers were significantly younger than noncarriers (41 vs. 50 years, P < 0.05). In silico algorithms predicted five variants to be deleterious. For two variants that were predicted to be benign, in vitro modeling demonstrated that these mutations conferred increased migratory capability, suggesting pathogenicity. Hispanic/Latino patients with gastric cancer possess unique genomic landscapes, including a high rate of CDH1 germline variants that may partially explain their aggressive clinical phenotypes. Individualized screening, genetic counseling, and treatment protocols based on patient ethnicity and race may be necessary. SIGNIFICANCE: Gastric cancer in Hispanic/Latino patients has unique genomic profiles that may contribute to the aggressive clinical phenotypes seen in these patients.

DNA Methylation Regulates Alternative Polyadenylation via CTCF and the Cohesin Complex.

Dysregulation of DNA methylation and mRNA alternative cleavage and polyadenylation (APA) are both prevalent in cancer and have been studied as independent processes. We discovered a DNA methylation-regulated APA mechanism when we compared genome-wide DNA methylation and polyadenylation site usage between DNA methylation-competent and DNA methylation-deficient cells. Here, we show that removal of DNA methylation enables CTCF binding and recruitment of the cohesin complex, which, in turn, form chromatin loops that promote proximal polyadenylation site usage. In this DNA demethylated context, either deletion of the CTCF binding site or depletion of RAD21 cohesin complex protein can recover distal polyadenylation site usage. Using data from The Cancer Genome Atlas, we authenticated the relationship between DNA methylation and mRNA polyadenylation isoform expression in vivo. This DNA methylation-regulated APA mechanism demonstrates how aberrant DNA methylation impacts transcriptome diversity and highlights the potential sequelae of global DNA methylation inhibition as a cancer treatment.

Diverse AR Gene Rearrangements Mediate Resistance to Androgen Receptor Inhibitors in Metastatic Prostate Cancer.

PURPOSE: Prostate cancer is the second leading cause of male cancer deaths. Castration-resistant prostate cancer (CRPC) is a lethal stage of the disease that emerges when endocrine therapies are no longer effective at suppressing activity of the androgen receptor (AR) transcription factor. The purpose of this study was to identify genomic mechanisms that contribute to the development and progression of CRPC. EXPERIMENTAL DESIGN: We used whole-genome and targeted DNA-sequencing approaches to identify mechanisms underlying CRPC in an aggregate cohort of 272 prostate cancer patients. We analyzed structural rearrangements at the genome-wide level and carried out a detailed structural rearrangement analysis of the AR locus. We used genome engineering to perform experimental modeling of AR gene rearrangements and long-read RNA sequencing to analyze effects on expression of AR and truncated AR variants (AR-V). RESULTS: AR was among the most frequently rearranged genes in CRPC tumors. AR gene rearrangements promoted expression of diverse AR-V species. AR gene rearrangements occurring in the context of AR amplification correlated with AR overexpression. Cell lines with experimentally derived AR gene rearrangements displayed high expression of tumor-specific AR-Vs and were resistant to endocrine therapies, including the AR antagonist enzalutamide. CONCLUSIONS: AR gene rearrangements are an important mechanism of resistance to endocrine therapies in CRPC.

Computerized Classification of Prostate Cancer Gleason Scores from Whole Slide Images.

Histological Gleason grading of tumor patterns is one of the most powerful prognostic predictors in prostate cancer. However, manual analysis and grading performed by pathologists are typically subjective and time-consuming. In this paper, we present an automatic technique for Gleason grading of prostate cancer from H&E stained whole slide pathology images using a set of novel completed and statistical local binary pattern (CSLBP) descriptors. First, the technique divides the whole slide image (WSI) into a set of small image tiles, where salient tumor tiles with high nuclei densities are selected for analysis. The CSLBP texture features that encode pixel intensity variations from circularly surrounding neighborhoods are extracted from salient image tiles to characterize different Gleason patterns. Finally, the CSLBP texture features computed from all tiles are integrated and utilized by the multi-class support vector machine (SVM) that assigns patient slides with different Gleason scores such as 6, 7, or ≥ 8. Experiments have been performed on 312 different patient cases selected from the cancer genome atlas (TCGA) and have achieved superior performances over state-of-the-art texture descriptors and baseline methods including deep learning models for prostate cancer Gleason grading.

Bayesian semi-nonnegative matrix tri-factorization to identify pathways associated with cancer phenotypes.

Accurate identification of pathways associated with cancer phenotypes (e.g., cancer subtypes and treatment outcomes) could lead to discovering reliable prognostic and/or predictive biomarkers for better patients stratification and treatment guidance. In our previous work, we have shown that non-negative matrix tri-factorization (NMTF) can be successfully applied to identify pathways associated with specific cancer types or disease classes as a prognostic and predictive biomarker. However, one key limitation of non-negative factorization methods, including various non-negative bi-factorization methods, is their limited ability to handle negative input data. For example, many types of molecular data that consist of real-values containing both positive and negative values (e.g., normalized/log transformed gene expression data where negative values represent down-regulated expression of genes) are not suitable input for these algorithms. In addition, most previous methods provide just a single point estimate and hence cannot deal with uncertainty effectively.To address these limitations, we propose a Bayesian semi-nonnegative matrix trifactorization method to identify pathways associated with cancer phenotypes from a realvalued input matrix, e.g., gene expression values. Motivated by semi-nonnegative factorization, we allow one of the factor matrices, the centroid matrix, to be real-valued so that each centroid can express either the up- or down-regulation of the member genes in a pathway. In addition, we place structured spike-and-slab priors (which are encoded with the pathways and a gene-gene interaction (GGI) network) on the centroid matrix so that even a set of genes that is not initially contained in the pathways (due to the incompleteness of the current pathway database) can be involved in the factorization in a stochastic way specifically, if those genes are connected to the member genes of the pathways on the GGI network. We also present update rules for the posterior distributions in the framework of variational inference. As a full Bayesian method, our proposed method has several advantages over the current NMTF methods, which are demonstrated using synthetic datasets in experiments. Using the The Cancer Genome Atlas (TCGA) gastric cancer and metastatic gastric cancer immunotherapy clinical-trial datasets, we show that our method could identify biologically and clinically relevant pathways associated with the molecular subtypes and immunotherapy response, respectively. Finally, we show that those pathways identified by the proposed method could be used as prognostic biomarkers to stratify patients with distinct survival outcome in two independent validation datasets. Additional information and codes can be found at https://github.com/parks-cs-ccf/BayesianSNMTF.

HSD3B1(1245A>C) variant regulates dueling abiraterone metabolite effects in prostate cancer.

BACKGROUND: A common germline variant in HSD3B1(1245A>C) encodes for a hyperactive 3ß-hydroxysteroid dehydrogenase 1 (3ßHSD1) missense that increases metabolic flux from extragonadal precursor steroids to DHT synthesis in prostate cancer. Enabling of extragonadal DHT synthesis by HSD3B1(1245C) predicts for more rapid clinical resistance to castration and sensitivity to extragonadal androgen synthesis inhibition. HSD3B1(1245C) thus appears to define a subgroup of patients who benefit from blocking extragonadal androgens. However, abiraterone, which is administered to block extragonadal androgens, is a steroidal drug that is metabolized by 3ßHSD1 to multiple steroidal metabolites, including 3-keto-5α-abiraterone, which stimulates the androgen receptor. Our objective was to determine if HSD3B1(1245C) inheritance is associated with increased 3-keto-5α-abiraterone synthesis in patients. METHODS: First, we characterized the pharmacokinetics of 7 steroidal abiraterone metabolites in 15 healthy volunteers. Second, we determined the association between serum 3-keto-5α-abiraterone levels and HSD3B1 genotype in 30 patients treated with abiraterone acetate (AA) after correcting for the determined pharmacokinetics. RESULTS: Patients who inherit 0, 1, and 2 copies of HSD3B1(1245C) have a stepwise increase in normalized 3-keto-5α-abiraterone (0.04 ng/ml, 2.60 ng/ml, and 2.70 ng/ml, respectively; P = 0.002). CONCLUSION: Increased generation of 3-keto-5α-abiraterone in patients with HSD3B1(1245C) might partially negate abiraterone benefits in these patients who are otherwise more likely to benefit from CYP17A1 inhibition. FUNDING: Prostate Cancer Foundation Challenge Award, National Cancer Institute.

Chemistry-First Approach for Nomination of Personalized Treatment in Lung Cancer.

Diversity in the genetic lesions that cause cancer is extreme. In consequence, a pressing challenge is the development of drugs that target patient-specific disease mechanisms. To address this challenge, we employed a chemistry-first discovery paradigm for de novo identification of druggable targets linked to robust patient selection hypotheses. In particular, a 200,000 compound diversity-oriented chemical library was profiled across a heavily annotated test-bed of >100 cellular models representative of the diverse and characteristic somatic lesions for lung cancer. This approach led to the delineation of 171 chemical-genetic associations, shedding light on the targetability of mechanistic vulnerabilities corresponding to a range of oncogenotypes present in patient populations lacking effective therapy. Chemically addressable addictions to ciliogenesis in TTC21B mutants and GLUT8-dependent serine biosynthesis in KRAS/KEAP1 double mutants are prominent examples. These observations indicate a wealth of actionable opportunities within the complex molecular etiology of cancer.