Genomic Landscape of Adenocarcinomas Across the Gastroesophageal Junction: Moving on from the Siewert Classification.

OBJECTIVE: To investigate how the Siewert classification of gastroesophageal junction adenocarcinomas correlates with genomic profiles. SUMMARY/BACKGROUND DATA: Current staging and treatment guidelines recommend that tumors with an epicenter less than 2 cm into the gastric cardia be treated as esophageal cancers, while tumors with epicenter greater than 2 cm into the cardia be staged and treated as gastric cancers. To date, however, few studies have compared the genomic profiles of the 3 Siewert classification groups to validate this distinction. METHODS: Using targeted tumor sequencing data on patients with adenocarcinoma of the gastroesophageal junction previously treated with surgery at our institution, we compared genomic features across Siewert classification groups. RESULTS: A total of 350 patients were included: 121 had Siewert type I, 170 type II, and 59 type III. Comparisons by Siewert location revealed that Siewert type I and II were primarily characterized as the chromosomal instability (CIN) molecular subtype and displayed Barrett's metaplasia and p53 and cell cycle pathway dysregulation. Siewert type III tumors, by contrast, were more heterogeneous, including higher proportions of microsatellite instability (MSI) and genomically stable (GS) tumors and more frequently displayed ARID1A and somatic CDH1 alterations, signet ring cell features, and poor differentiation. Overall, Siewert type I and II tumors demonstrated greater genomic overlap with lower esophageal tumors, while Siewert type III tumors shared genomic features with gastric tumors. CONCLUSIONS: Overall, our results support recent updates in treatment and staging guidelines. Ultimately, however, molecular rather than anatomic classification may prove more valuable in determining staging, treatment, and prognosis.

A Novel Approach to Quantify Heterogeneity of Intrahepatic Cholangiocarcinoma: the Hidden-Genome Classifier.

PURPOSE: Intrahepatic cholangiocarcinoma (IHC) are heterogeneous tumors. The hidden-genome classifier, a supervised machine learning-based algorithm, was used to quantify tumor heterogeneity and improve classification. EXPERIMENTAL DESIGN: A retrospective review of 1370 patients with IHC, extrahepatic cholangiocarcinoma (EHC), gallbladder cancer (GBC), hepatocellular carcinoma (HCC), or biphenotypic tumors was conducted. A hidden-genome model classified 527 IHCs based on genetic similarity to EHC/GBC or HCC. Genetic, histologic, and clinical data were correlated. RESULTS: 410 IHC (78%) had >50% genetic homology with EHC/GBC; 122 (23%) had >90% homology ("biliary-class"), characterized by alterations of KRAS, SMAD4, and CDKN2A loss. 117 IHC (22%) had >50% genetic homology with HCC; 30 (5.7%) had >90% homology ("HCC-class"), characterized by TERT alterations. Patients with biliary- vs. non-biliary-class IHC had median overall survival (OS) of 1 year (95% CI: 0.77, 1.5) vs. 1.8 years (95% CI: 1.6, 2.0) for unresectable disease and 2.4 years (95% CI: 2.1, NR) vs. 5.1 years (95% CI: 4.8, 6.9) for resectable disease. Large-duct-IHC (n=28) was more common in the biliary-class (n=27); HCC-class was comprised mostly of small-duct-IHC (64%, p=0.02). The hidden-genomic classifier predicted OS independent of FGFR2 and IDH1 alterations. By contrast, the histology subtype did not predict OS. CONCLUSIONS: IHC genetics form a spectrum with worse OS for tumors genetically aligned with EHC/GBC. The classifier proved superior to histologic subtypes for predicting OS independent of FGFR2 and IDH1 alterations. These results may explain the differential treatment responses seen in IHC and may direct therapy by help stratifing patients in future clinical trials.

Natural History and Genomic Landscape of Chemotherapy-Resistant Muscle-Invasive Bladder Cancer.

PURPOSE: Patients with residual invasive bladder cancer after neoadjuvant chemotherapy (NAC) and radical cystectomy have a poor prognosis. Data on adjuvant therapy for these patients are conflicting. We sought to evaluate the natural history and genomic landscape of chemotherapy-resistant bladder cancer to inform patient management and clinical trials. METHODS: Data were collected on patients with clinically localized muscle-invasive urothelial bladder cancer treated with NAC and cystectomy at our institution between May 15, 2001, and August 15, 2019, and completed four cycles of gemcitabine and cisplatin NAC, excluding those treated with adjuvant therapies. Survival was estimated using the Kaplan-Meier method, and multivariable Cox proportional hazards models were used to identify predictors of recurrence-free survival (RFS). Genomic alterations were identified in targeted exome sequencing (Memorial Sloan Kettering Integrated Mutation Profiling of Actionable Cancer Targets) data from post-NAC specimens from a subset of patients. RESULTS: Lymphovascular invasion (LVI) was the strongest predictor of RFS (hazard ratio, 2.15 [95% CI, 1.37 to 3.39]) on multivariable analysis. Patients with ypT2N0 disease without LVI had a significantly prolonged RFS compared with those with LVI (70% RFS at 5 years). Lymph node yield did not affect RFS. Among patients with sequencing data (n = 101), chemotherapy-resistant tumors had fewer alterations in DNA damage response genes compared with tumors from a publicly available chemotherapy-naïve cohort (15% v 29%; P = .021). Alterations in CDKN2A/B were associated with shorter RFS. PIK3CA alterations were associated with LVI. Potentially actionable alterations were identified in more than 75% of tumors. CONCLUSION: Although chemotherapy-resistant bladder cancer generally portends a poor prognosis, patients with organ-confined disease without LVI may be candidates for close observation without adjuvant therapy. The genomic landscape of chemotherapy-resistant tumors is similar to chemotherapy-naïve tumors. Therapeutic opportunities exist for targeted therapies as adjuvant treatment in chemotherapy-resistant disease.

Precision medicine for pancreatic cancer: Characterizing the clinico-genomic landscape and outcomes of KRAS G12C-mutated disease.

BACKGROUND: Mutated KRAS is the most common oncogene alteration in pancreatic cancer (PDAC), and KRAS G12C mutations (KRAS G12Cmut) are observed in 1-2%. Several inhibitors of KRAS G12C have recently demonstrated promise in solid tumors, including PDAC. Little is known regarding clinical, genomics and outcome data of this population. METHODS: Patients with PDAC and KRAS G12Cmut were identified at Memorial Sloan Kettering Cancer Center (MSK), and via the AACR Project GENIE database. Clinical, treatment, genomic and outcomes data were analysed. A cohort of patients at MSK with non-G12C KRAS PDAC was included for comparison. RESULTS: Among 3,571 patients with PDAC, 39 with KRAS G12Cmut were identified (1.1%). Median age was 67 years, 56% were female. Median BMI was 29.2 kg/m2, 67% had a smoking history. Median OS 13 months (9.4, not reached (NR)) for stage IV, and 26 months (23, NR) for stage I-III. Complete genomic data (via AACR GENIE) was available for N = 74. Most common co-alterations included: TP53 (73%), CDKN2A (33%), SMAD4 (28%), and ARID1A (21%). Compared with a large cohort (N = 2931) of non-G12C KRAS-mutated PDAC, ARID1A co-mutations were more frequent in KRAS G12Cmut (P < .05). OS did not differ between KRAS G12Cmut and non-G12C KRAS PDAC. Germline pathogenic variants were identified in 17%. N = 2 received KRAS G12C-directed therapy. CONCLUSION: PDAC and KRAS G12Cmut may be associated with a distinct clinical phenotype. Genomic features are similar to non-G12C KRAS-mutated PDAC, although enrichment of ARID1A co-mutations was observed. Targeting of KRAS G12C in PDAC provides a precedent for broader KRAS targeting in PDAC.

Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions.

Artificial intelligence (AI) in oncology is advancing beyond algorithm development to integration into clinical practice. This review describes the current state of the field, with a specific focus on clinical integration. AI applications are structured according to cancer type and clinical domain, focusing on the four most common cancers and tasks of detection, diagnosis, and treatment. These applications encompass various data modalities, including imaging, genomics, and medical records. We conclude with a summary of existing challenges, evolving solutions, and potential future directions for the field. SIGNIFICANCE: AI is increasingly being applied to all aspects of oncology, where several applications are maturing beyond research and development to direct clinical integration. This review summarizes the current state of the field through the lens of clinical translation along the clinical care continuum. Emerging areas are also highlighted, along with common challenges, evolving solutions, and potential future directions for the field.

Molecular and Clinical Determinants of Acquired Resistance and Treatment Duration for Targeted Therapies in Colorectal Cancer.

PURPOSE: Targeted therapies have improved outcomes for patients with metastatic colorectal cancer, but their impact is limited by rapid emergence of resistance. We hypothesized that an understanding of the underlying genetic mechanisms and intrinsic tumor features that mediate resistance to therapy will guide new therapeutic strategies and ultimately allow the prevention of resistance. EXPERIMENTAL DESIGN: We assembled a series of 52 patients with paired pretreatment and progression samples who received therapy targeting EGFR (n = 17), BRAF V600E (n = 17), KRAS G12C (n = 15), or amplified HER2 (n = 3) to identify molecular and clinical factors associated with time on treatment (TOT). RESULTS: All patients stopped treatment for progression and TOT did not vary by oncogenic driver (P = 0.5). Baseline disease burden (≥3 vs. <3 sites, P = 0.02), the presence of hepatic metastases (P = 0.02), and gene amplification on baseline tissue (P = 0.03) were each associated with shorter TOT. We found evidence of chromosomal instability (CIN) at progression in patients with baseline MAPK pathway amplifications and those with acquired gene amplifications. At resistance, copy-number changes (P = 0.008) and high number (≥5) of acquired alterations (P = 0.04) were associated with shorter TOT. Patients with hepatic metastases demonstrated both higher number of emergent alterations at resistance and enrichment of mutations involving receptor tyrosine kinases. CONCLUSIONS: Our genomic analysis suggests that high baseline CIN or effective induction of enhanced mutagenesis on targeted therapy underlies rapid progression. Longer response appears to result from a progressive acquisition of genomic or chromosomal instability in the underlying cancer or from the chance event of a new resistance alteration.

Integrative Molecular Analyses of the MD Anderson Prostate Cancer Patient-derived Xenograft (MDA PCa PDX) Series.

PURPOSE: Develop and deploy a robust discovery platform that encompasses heterogeneity, clinical annotation, and molecular characterization and overcomes the limited availability of prostate cancer models. This initiative builds on the rich MD Anderson (MDA) prostate cancer (PCa) patient-derived xenograft (PDX) resource to complement existing publicly available databases by addressing gaps in clinically annotated models reflecting the heterogeneity of potentially lethal and lethal prostate cancer. EXPERIMENTAL DESIGN: We performed whole-genome, targeted, and RNA sequencing in representative samples of the same tumor from 44 PDXs derived from 38 patients linked to donor tumor metadata and corresponding organoids. The cohort includes models derived from different morphologic groups, disease states, and involved organ sites (including circulating tumor cells), as well as paired samples representing heterogeneity or stages before and after therapy. RESULTS: The cohort recapitulates clinically reported alterations in prostate cancer genes, providing a data resource for clinical and molecular interrogation of suitable experimental models. Paired samples displayed conserved molecular alteration profiles, suggesting the relevance of other regulatory mechanisms (e.g., epigenomic) influenced by the microenvironment and/or treatment. Transcriptomically, models were grouped on the basis of morphologic classification. DNA damage response-associated mechanisms emerged as differentially regulated between adenocarcinoma and neuroendocrine prostate cancer in a cross-interrogation of PDX/patient datasets. CONCLUSIONS: We addressed the gap in clinically relevant prostate cancer models through comprehensive molecular characterization of MDA PCa PDXs, providing a discovery platform that integrates with patient data and benchmarked to therapeutically relevant consensus clinical groupings. This unique resource supports robust hypothesis generation and testing from basic, translational, and clinical perspectives.

Deep-Learning Model for Tumor-Type Prediction Using Targeted Clinical Genomic Sequencing Data.

Tumor type guides clinical treatment decisions in cancer, but histology-based diagnosis remains challenging. Genomic alterations are highly diagnostic of tumor type, and tumor-type classifiers trained on genomic features have been explored, but the most accurate methods are not clinically feasible, relying on features derived from whole-genome sequencing (WGS), or predicting across limited cancer types. We use genomic features from a data set of 39,787 solid tumors sequenced using a clinically targeted cancer gene panel to develop Genome-Derived-Diagnosis Ensemble (GDD-ENS): a hyperparameter ensemble for classifying tumor type using deep neural networks. GDD-ENS achieves 93% accuracy for high-confidence predictions across 38 cancer types, rivaling the performance of WGS-based methods. GDD-ENS can also guide diagnoses of rare type and cancers of unknown primary and incorporate patient-specific clinical information for improved predictions. Overall, integrating GDD-ENS into prospective clinical sequencing workflows could provide clinically relevant tumor-type predictions to guide treatment decisions in real time. SIGNIFICANCE: We describe a highly accurate tumor-type prediction model, designed specifically for clinical implementation. Our model relies only on widely used cancer gene panel sequencing data, predicts across 38 distinct cancer types, and supports integration of patient-specific nongenomic information for enhanced decision support in challenging diagnostic situations. See related commentary by Garg, p. 906. This article is featured in Selected Articles from This Issue, p. 897.

Genomic Characterization and Clinical Outcomes of Patients with Peritoneal Metastases from the AACR GENIE Biopharma Collaborative Colorectal Cancer Registry.

Peritoneal metastases (PM) are common in metastatic colorectal cancer (mCRC). We aimed to characterize patients with mCRC and PM from a clinical and molecular perspective using the American Association of Cancer Research Genomics Evidence Neoplasia Information Exchange (GENIE) Biopharma Collaborative (BPC) registry. Patients' tumor samples underwent targeted next-generation sequencing. Clinical characteristics and treatment outcomes were collected retrospectively. Overall survival (OS) from advanced disease and progression-free survival (PFS) from start of cancer-directed drug regimen were estimated and adjusted for the left truncation bias. A total of 1,281 patients were analyzed, 244 (19%) had PM at time of advanced disease. PM were associated with female sex [OR: 1.67; 95% confidence interval (CI): 1.11-2.54; P = 0.014] and higher histologic grade (OR: 1.72; 95% CI: 1.08-2.71; P = 0.022), while rectal primary tumors were less frequent in patients with PM (OR: 0.51; 95% CI: 0.29-0.88; P < 0.001). APC occurred less frequently in patients with PM (N = 151, 64% vs. N = 788, 79%) while MED12 alterations occurred more frequently in patients with PM (N = 20, 10% vs. N = 32, 4%); differences in MED12 were not significant when restricting to oncogenic and likely oncogenic variants according to OncoKB. Patients with PM had worse OS (HR: 1.45; 95% CI: 1.16-1.81) after adjustment for independently significant clinical and genomic predictors. PFS from initiation of first-line treatment did not differ by presence of PM. In conclusion, PM were more frequent in females and right-sided primary tumors. Differences in frequencies of MED12 and APC alterations were identified between patients with and without PM. PM were associated with shorter OS but not with PFS from first-line treatment. SIGNIFICANCE: Utilizing the GENIE BPC registry, this study found that PM in patients with colorectal cancer occur more frequently in females and right-sided primary tumors and are associated with worse OS. In addition, we found a lower frequency of APC alterations and a higher frequency in MED12 alterations in patients with PM.

Obesity shapes selection for driver mutations in cancer.

Obesity is a leading risk factor for cancer, but whether obesity is linked to specific genomic subtypes of cancer is unknown. Here, we examined the relationship between obesity and tumor genotype in two large clinicogenomic corpora. Obesity was associated with specific driver mutations in lung adenocarcinoma, endometrial carcinoma, and cancers of unknown primary, independent of clinical covariates and genetic ancestry. Obesity is therefore a putative driver of etiologic heterogeneity across cancers.

Clinical and molecular characteristics of early-onset vs average-onset esophagogastric cancer.

BACKGROUND: The rate of esophagogastric cancer is rising among individuals under 50 years of age. It remains unknown whether early-onset esophagogastric cancer represents a unique entity. This study investigated the clinical and molecular characteristics of early-onset and average-onset esophagogastric cancer . METHODS: We reviewed the Memorial Sloan Kettering Cancer Center gastric, esophageal, and gastroesophageal junction cancer database. Associations between baseline characteristics and tumor and germline molecular alterations were compared between those with early-onset and average-onset esophagogastric cancer using Fisher exact tests and the Benjamini-Hochberg method for multiple-hypothesis correction. RESULTS: We included 1123 patients with early-onset esophagogastric cancer (n = 219; median age = 43 years [range = 18-49 years]) and average-onset esophagogastric cancer (n = 904; median age = 67 years [range = 50-94 years]) treated between 2005 and 2018. The early-onset group had more women (39% vs 28%, P = .002). Patients with early-onset esophagogastric cancer were more likely to have a gastric primary site (64% vs 44%, P < .0001). The signet ring cell and/or diffuse type was 3 times more common in the early-onset esophagogastric cancer group (31% vs 9%, P < .0001). Early-onsite tumors were more frequently genomically stable (31% vs 18%, P = .0002) and unlikely to be microsatellite instability high (2% vs 7%, P = .003). After restricting to adenocarcinoma and signet ring cell and/or diffuse type carcinomas, we observed no difference in stage (P = .40) or overall survival from stage IV diagnosis (median = 22.7 vs 22.1 months, P = .78). CONCLUSIONS: Our study supported a preponderance of gastric primary disease sites, signet ring histology, and genomically stable molecular subtypes in early-onset esophagogastric cancer. Our findings highlight the need for further research to define the underlying pathogenesis and strategies for early detection and prevention.

Quantifying the Expanding Landscape of Clinical Actionability for Patients with Cancer.

There is a continuing debate about the proportion of cancer patients that benefit from precision oncology, attributable in part to conflicting views as to which molecular alterations are clinically actionable. To quantify the expansion of clinical actionability since 2017, we annotated 47,271 solid tumors sequenced with the MSK-IMPACT clinical assay using two temporally distinct versions of the OncoKB knowledge base deployed 5 years apart. Between 2017 and 2022, we observed an increase from 8.9% to 31.6% in the fraction of tumors harboring a standard care (level 1 or 2) predictive biomarker of therapy response and an almost halving of tumors carrying nonactionable drivers (44.2% to 22.8%). In tumors with limited or no clinical actionability, TP53 (43.2%), KRAS (19.2%), and CDKN2A (12.2%) were the most frequently altered genes. SIGNIFICANCE: Although clear progress has been made in expanding the availability of precision oncology-based treatment paradigms, our results suggest a continued unmet need for innovative therapeutic strategies, particularly for cancers with currently undruggable oncogenic drivers. See related commentary by Horak and Fröhling, p. 18. This article is featured in Selected Articles from This Issue, p. 5.

Pathogenic germline variants in non-BRCA1/2 homologous recombination genes in ovarian cancer: Analysis of tumor phenotype and survival.

OBJECTIVE: To define molecular features of ovarian cancer (OC) with germline pathogenic variants (PVs) in non-BRCA homologous recombination (HR) genes and analyze survival compared to BRCA1/2 and wildtype (WT) OC. METHODS: We included patients with OC undergoing tumor-normal sequencing (MSK-IMPACT) from 07/01/2015-12/31/2020, including germline assessment of BRCA1/2 and other HR genes ATM, BARD1, BRIP1, FANCA, FANCC, NBN, PALB2, RAD50, RAD51B, RAD51C, and RAD51D. Biallelic inactivation was assessed within tumors. Progression-free (PFS) and overall survival (OS) were calculated from pathologic diagnosis using the Kaplan-Meier method with left truncation. Whole-exome sequencing (WES) was performed in a subset. RESULTS: Of 882 patients with OC, 56 (6.3%) had germline PVs in non-BRCA HR genes; 95 (11%) had BRCA1-associated OC (58 germline, 37 somatic); and 59 (6.7%) had BRCA2-associated OC (40 germline, 19 somatic). High rates of biallelic alterations were observed among germline PVs in BRIP1 (11/13), PALB2 (3/4), RAD51B (3/4), RAD51C (3/4), and RAD51D (8/10). In cases with WES (27/35), there was higher tumor mutational burden (TMB; median 2.5 [1.1-6.0] vs. 1.2 mut/Mb [0.6-2.6]) and enrichment of HR-deficient (HRD) mutational signatures in tumors associated with germline PALB2 and RAD51B/C/D compared with BRIP1 PVs (p < 0.01). Other features of HRD, including telomeric-allelic imbalance (TAI) and large-scale state transitions (LSTs), were similar. Although there was heterogeneity in PFS/OS by gene group, only BRCA1/2-associated OC had improved survival compared to WT OC (p < 0.01). CONCLUSIONS: OCs associated with germline PVs in non-BRCA HR genes represent a heterogenous group, with PALB2 and RAD51B/C/D associated with an HRD phenotype.

Tumor sequencing of African ancestry reveals differences in clinically relevant alterations across common cancers.

Cancer genomes from patients with African (AFR) ancestry have been poorly studied in clinical research. We leverage two large genomic cohorts to investigate the relationship between genomic alterations and AFR ancestry in six common cancers. Cross-cancer type associations, such as an enrichment of MYC amplification with AFR ancestry in lung, breast, and prostate cancers, and depletion of BRAF alterations are observed in colorectal and pancreatic cancers. There are differences in actionable alterations, such as depletion of KRAS G12C and EGFR L858R, and enrichment of ROS1 fusion with AFR ancestry in lung cancers. Interestingly, in lung cancer, KRAS mutations are less common in both smokers and non-smokers with AFR ancestry, whereas the association of TP53 mutations with AFR ancestry is only seen in smokers, suggesting an ancestry-environment interaction that modifies driver rates. Our study highlights the need to increase representation of patients with AFR ancestry in drug development and biomarker discovery.

Deep Learning Model for Tumor Type Prediction using Targeted Clinical Genomic Sequencing Data.

Tumor type guides clinical treatment decisions in cancer, but histology-based diagnosis remains challenging. Genomic alterations are highly diagnostic of tumor type, and tumor type classifiers trained on genomic features have been explored, but the most accurate methods are not clinically feasible, relying on features derived from whole genome sequencing (WGS), or predicting across limited cancer types. We use genomic features from a dataset of 39,787 solid tumors sequenced using a clinical targeted cancer gene panel to develop Genome-Derived-Diagnosis Ensemble (GDD-ENS): a hyperparameter ensemble for classifying tumor type using deep neural networks. GDD-ENS achieves 93% accuracy for high-confidence predictions across 38 cancer types, rivalling performance of WGS-based methods. GDD-ENS can also guide diagnoses on rare type and cancers of unknown primary, and incorporate patient-specific clinical information for improved predictions. Overall, integrating GDD-ENS into prospective clinical sequencing workflows has enabled clinically-relevant tumor type predictions to guide treatment decisions in real time.

Genomic analysis and clinical correlations of non-small cell lung cancer brain metastasis.

Up to 50% of patients with non-small cell lung cancer (NSCLC) develop brain metastasis (BM), yet the study of BM genomics has been limited by tissue access, incomplete clinical data, and a lack of comparison with paired extracranial specimens. Here we report a cohort of 233 patients with resected and sequenced (MSK-IMPACT) NSCLC BM and comprehensive clinical data. With matched samples (47 primary tumor, 42 extracranial metastatic), we show CDKN2A/B deletions and cell cycle pathway alterations to be enriched in the BM samples. Meaningful clinico-genomic correlations are noted, namely EGFR alterations in leptomeningeal disease (LMD) and MYC amplifications in multifocal regional brain progression. Patients who developed early LMD frequently have had uncommon, multiple, and persistently detectable EGFR driver mutations. The distinct mutational patterns identified in BM specimens compared to other tissue sites suggest specific biologic underpinnings of intracranial progression.

Cooperative Genomic Lesions in HRAS-Mutant Cancers Predict Resistance to Farnesyltransferase Inhibitors.

The clinical development of farnesyltransferase inhibitors (FTI) for HRAS-mutant tumors showed mixed responses dependent on cancer type. Co-occurring mutations may affect response. We aimed to uncover cooperative genetic events specific to HRAS-mutant tumors and study their effect on FTI sensitivity. Using targeted sequencing data from MSK-IMPACT and DFCI-GENIE databases we identified co-mutations in HRAS- vs KRAS- and NRAS-mutant cancers. HRAS-mutant cancers had a higher frequency of co-altered mutations (48.8%) in MAPK, PI3K, or RTK pathways genes compared to KRAS- and NRAS-mutant cancers (41.4% and 38.4%, respectively; p < 0.05). Class 3 BRAF, NF1, PTEN, and PIK3CA mutations were more prevalent in HRAS-mutant lineages. To study the effect of comutations on FTI sensitivity, HrasG13R was transfected into 'RASless' (Kraslox/lox;Hras-/-;Nras-/-) mouse embryonic fibroblasts (MEFs) which sensitized non-transfected MEFs to tipifarnib. Comutation in the form of Pten or Nf1 deletion or Pik3caH1047R or BrafG466E transduction led to relative resistance to tipifarnib in HrasG13R MEFs in the presence or absence of KrasWT. Combined treatment of tipifarnib with MEK inhibition sensitized cells to tipifarnib, including in MEFs with PI3K pathway comutations. HRAS-mutant tumors demonstrate lineage demonstrate lineage-dependent MAPK/PI3K pathway alterations that confer relative resistance to tipifarnib. Combined FTI and MEK inhibition is a promising combination for HRAS-mutant tumors.

Unique Genomic Alterations and Microbial Profiles Identified in Patients With Gastric Cancer of African, European, and Asian Ancestry: A Novel Path for Precision Oncology.

OBJECTIVE: Here, we characterize differences in the genetic and microbial profiles of GC in patients of African (AFR), European, and Asian ancestry. BACKGROUND: Gastric cancer (GC) is a heterogeneous disease with clinicopathologic variations due to a complex interplay of environmental and biological factors, which may affect disparities in oncologic outcomes.. METHODS: We identified 1042 patients with GC with next-generation sequencing data from an institutional Integrated Mutation Profiling of Actionable Cancer Targets assay and the Cancer Genomic Atlas group. Genetic ancestry was inferred from markers captured by the Integrated Mutation Profiling of Actionable Cancer Targets and the Cancer Genomic Atlas whole exome sequencing panels. Tumor microbial profiles were inferred from sequencing data using a validated microbiome bioinformatics pipeline. Genomic alterations and microbial profiles were compared among patients with GC of different ancestries. RESULTS: We assessed 8023 genomic alterations. The most frequently altered genes were TP53 , ARID1A , KRAS , ERBB2 , and CDH1 . Patients of AFR ancestry had a significantly higher rate of CCNE1 alterations and a lower rate of KRAS alterations ( P < 0.05), and patients of East Asian ancestry had a significantly lower rate of PI3K pathway alterations ( P < 0.05) compared with other ancestries. Microbial diversity and enrichment did not differ significantly across ancestry groups ( P > 0.05). CONCLUSIONS: Distinct patterns of genomic alterations and variations in microbial profiles were identified in patients with GC of AFR, European, and Asian ancestry. Our findings of variation in the prevalence of clinically actionable tumor alterations among ancestry groups suggest that precision medicine can mitigate oncologic disparities.

Determinants of Survival with Combined HER2 and PD-1 Blockade in Metastatic Esophagogastric Cancer.

PURPOSE: We report updated clinical outcomes from a phase II study of pembrolizumab, trastuzumab, and chemotherapy (PTC) in metastatic esophagogastric cancer in conjunction with outcomes from an independent Memorial Sloan Kettering (MSK) cohort. PATIENTS AND METHODS: The significance of pretreatment 89Zr-trastuzumab PET, plasma circulating tumor DNA (ctDNA) dynamics, and tumor HER2 expression and whole exome sequencing was evaluated to identify prognostic biomarkers and mechanisms of resistance in patients treated on-protocol with PTC. Additional prognostic features were evaluated using a multivariable Cox regression model of trastuzumab-treated MSK patients (n = 226). Single-cell RNA sequencing (scRNA-seq) data from MSK and Samsung were evaluated for mechanisms of therapy resistance. RESULTS: 89Zr-trastuzumab PET, scRNA-seq, and serial ctDNA with CT imaging identified how pre-treatment intrapatient genomic heterogeneity contributes to inferior progression-free survival (PFS). We demonstrated that the presence of intensely avid lesions by 89Zr-trastuzumab PET declines in tumor-matched ctDNA by 3 weeks, and clearance of tumor-matched ctDNA by 9 weeks were minimally invasive biomarkers of durable PFS. Paired pre- and on-treatment scRNA-seq identified rapid clearance of HER2-expressing tumor clones with expansion of clones expressing a transcriptional resistance program, which was associated with MT1H, MT1E, MT2A, and MSMB expression. Among trastuzumab-treated patients at MSK, ERBB2 amplification was associated with improved PFS, while alterations in MYC and CDKN2A/B were associated with inferior PFS. CONCLUSIONS: These findings highlight the clinical relevance of identifying baseline intrapatient heterogeneity and serial ctDNA monitoring of HER2-positive esophagogastric cancer patients to identify early evidence of treatment resistance, which could guide proactive therapy escalation or deescalation.

Immunometabolic coevolution defines unique microenvironmental niches in ccRCC.

Tumor cell phenotypes and anti-tumor immune responses are shaped by local metabolite availability, but intratumoral metabolite heterogeneity (IMH) and its phenotypic consequences remain poorly understood. To study IMH, we profiled tumor/normal regions from clear cell renal cell carcinoma (ccRCC) patients. A common pattern of IMH transcended all patients, characterized by correlated fluctuations in the abundance of metabolites and processes associated with ferroptosis. Analysis of intratumoral metabolite-RNA covariation revealed that the immune composition of the microenvironment, especially the abundance of myeloid cells, drove intratumoral metabolite variation. Motivated by the strength of RNA-metabolite covariation and the clinical significance of RNA biomarkers in ccRCC, we inferred metabolomic profiles from the RNA sequencing data of ccRCC patients enrolled in 7 clinical trials, and we ultimately identifyied metabolite biomarkers associated with response to anti-angiogenic agents. Local metabolic phenotypes, therefore, emerge in tandem with the immune microenvironment, influence ongoing tumor evolution, and are associated with therapeutic sensitivity.