LOXL2-mediated chromatin compaction is required to maintain the oncogenic properties of triple-negative breast cancer cells.

Oxidation of histone H3 at lysine 4 (H3K4ox) is catalyzed by lysyl oxidase homolog 2 (LOXL2). This histone modification is enriched in heterochromatin in triple-negative breast cancer (TNBC) cells and has been linked to the maintenance of compacted chromatin. However, the molecular mechanism underlying this maintenance is still unknown. Here, we show that LOXL2 interacts with RuvB-Like 1 (RUVBL1), RuvB-Like 2 (RUVBL2), Actin-like protein 6A (ACTL6A), and DNA methyltransferase 1associated protein 1 (DMAP1), a complex involved in the incorporation of the histone variant H2A.Z. Our experiments indicate that this interaction and the active form of RUVBL2 are required to maintain LOXL2-dependent chromatin compaction. Genome-wide experiments showed that H2A.Z, RUVBL2, and H3K4ox colocalize in heterochromatin regions. In the absence of LOXL2 or RUVBL2, global levels of the heterochromatin histone mark H3K9me3 were strongly reduced, and the ATAC-seq signal in the H3K9me3 regions was increased. Finally, we observed that the interplay between these series of events is required to maintain H3K4ox-enriched heterochromatin regions, which in turn is key for maintaining the oncogenic properties of the TNBC cell line tested (MDA-MB-231).

Weakly Supervised Deep Learning Predicts Immunotherapy Response in Solid Tumors Based on PD-L1 Expression.

Programmed death-ligand 1 (PD-L1) IHC is the most commonly used biomarker for immunotherapy response. However, quantification of PD-L1 status in pathology slides is challenging. Neither manual quantification nor a computer-based mimicking of manual readouts is perfectly reproducible, and the predictive performance of both approaches regarding immunotherapy response is limited. In this study, we developed a deep learning (DL) method to predict PD-L1 status directly from raw IHC image data, without explicit intermediary steps such as cell detection or pigment quantification. We trained the weakly supervised model on PD-L1-stained slides from the non-small cell lung cancer (NSCLC)-Memorial Sloan Kettering (MSK) cohort (N = 233) and validated it on the pan-cancer-Vall d'Hebron Institute of Oncology (VHIO) cohort (N = 108). We also investigated the performance of the model to predict response to immune checkpoint inhibitors (ICI) in terms of progression-free survival. In the pan-cancer-VHIO cohort, the performance was compared with tumor proportion score (TPS) and combined positive score (CPS). The DL model showed good performance in predicting PD-L1 expression (TPS ≥ 1%) in both NSCLC-MSK and pan-cancer-VHIO cohort (AUC 0.88 ± 0.06 and 0.80 ± 0.03, respectively). The predicted PD-L1 status showed an improved association with response to ICIs [HR: 1.5 (95% confidence interval: 1-2.3), P = 0.049] compared with TPS [HR: 1.4 (0.96-2.2), P = 0.082] and CPS [HR: 1.2 (0.79-1.9), P = 0.386]. Notably, our explainability analysis showed that the model does not just look at the amount of brown pigment in the IHC slides, but also considers morphologic factors such as lymphocyte conglomerates. Overall, end-to-end weakly supervised DL shows potential for improving patient stratification for cancer immunotherapy by analyzing PD-L1 IHC, holistically integrating morphology and PD-L1 staining intensity. SIGNIFICANCE: The weakly supervised DL model to predict PD-L1 status from raw IHC data, integrating tumor staining intensity and morphology, enables enhanced patient stratification in cancer immunotherapy compared with traditional pathologist assessment.

Early-Stage Breast Cancer Detection in Breast Milk.

Breast cancer occurring during pregnancy (PrBC) and postpartum (PPBC) is usually diagnosed at more advanced stages compared with other breast cancer, worsening its prognosis. PPBC is particularly aggressive, with increased metastatic risk and mortality. Thus, effective screening methods to detect early PrBC and PPBC are needed. We report for the first time that cell-free tumor DNA (ctDNA) is present in breast milk (BM) collected from patients with breast cancer. Analysis of ctDNA from BM detects tumor variants in 87% of the cases by droplet digital PCR, while variants remain undetected in 92% of matched plasma samples. Retrospective next-generation sequencing analysis in BM ctDNA recapitulates tumor variants, with an overall clinical sensitivity of 71.4% and specificity of 100%. In two cases, ctDNA was detectable in BM collected 18 and 6 months prior to standard diagnosis. Our results open up the potential use of BM as a new source for liquid biopsy for PPBC detection. SIGNIFICANCE: For the first time, we show that BM obtained from patients with breast cancer carries ctDNA, surpassing plasma-based liquid biopsy for detection and molecular profiling of early-stage breast cancer, even prior to diagnosis by image. See related commentary by Cunningham and Turner, p. 2125. This article is featured in Selected Articles from This Issue, p. 2109.

Comprehensive evaluation of surrogate endpoints to predict overall survival in trials with PD1/PD-L1 immune checkpoint inhibitors plus chemotherapy.

BACKGROUND: PD1/PD-L1 immune checkpoint inhibitors (ICI) have revolutionized cancer treatment. Although there is controversy about the accuracy of surrogate endpoints in the ICI setting to predict overall survival (OS), these endpoints are commonly used in confirmatory trials. Here we aimed to explore the validity of classical and novel surrogate endpoints in randomised controlled trials (RCT) that combine ICI plus chemotherapy (CT) in the first-line setting. MATERIAL AND METHODS: A systematic review was conducted to identify RCT investigating anti-PD1/PD-L1 drugs plus CT versus CT alone. We performed (i) arm-level analysis to evaluate predictors of median OS (mOS) and (ii) comparison-level analysis for OS hazard ratio (HR) estimations. Linear regression models weighted by trial size were fitted and adjusted R2 values were reported. RESULTS: Thirty-nine RCTs involving 22,341 patients met the inclusion criteria (17 non-small cell lung, 9 gastroesophageal and 13 in other cancers) with ten different ICI under study. Overall, ICI plus CT improved OS (HR = 0.76; 95%CI: 0.73-0.80). In the arm-level analysis, the best mOS prediction was obtained with a new endpoint that combines median duration of response and ORR (mDoR-ORR) and with median PFS (R2 = 0.5 both). In the comparison-level analysis, PFS HR showed a moderate association with OS HR (R2 = 0.52). Early OS read-outs were highly associated with final OS outcomes (R2 = 0.80). CONCLUSIONS: The association between surrogate endpoints and OS in first-line RCT combining anti-PD1/PD-L1 and CT is moderate-low. Early OS read-outs showed a good association with final OS HR while the mDOR-ORR endpoint could help to better design confirmatory trials after single-arm phase II trials.

High p16 expression and heterozygous RB1 loss are biomarkers for CDK4/6 inhibitor resistance in ER+ breast cancer.

CDK4/6 inhibitors combined with endocrine therapy have demonstrated higher antitumor activity than endocrine therapy alone for the treatment of advanced estrogen receptor-positive breast cancer. Some of these tumors are de novo resistant to CDK4/6 inhibitors and others develop acquired resistance. Here, we show that p16 overexpression is associated with reduced antitumor activity of CDK4/6 inhibitors in patient-derived xenografts (n = 37) and estrogen receptor-positive breast cancer cell lines, as well as reduced response of early and advanced breast cancer patients to CDK4/6 inhibitors (n = 89). We also identified heterozygous RB1 loss as biomarker of acquired resistance and poor clinical outcome. Combination of the CDK4/6 inhibitor ribociclib with the PI3K inhibitor alpelisib showed antitumor activity in estrogen receptor-positive non-basal-like breast cancer patient-derived xenografts, independently of PIK3CA, ESR1 or RB1 mutation, also in drug de-escalation experiments or omitting endocrine therapy. Our results offer insights into predicting primary/acquired resistance to CDK4/6 inhibitors and post-progression therapeutic strategies.

Preclinical In Vivo Validation of the RAD51 Test for Identification of Homologous Recombination-Deficient Tumors and Patient Stratification.

PARP inhibitors (PARPi) are approved drugs for platinum-sensitive, high-grade serous ovarian cancer (HGSOC) and for breast, prostate, and pancreatic cancers (PaC) harboring genetic alterations impairing homologous recombination repair (HRR). Detection of nuclear RAD51 foci in tumor cells is a marker of HRR functionality, and we previously established a test to detect RAD51 nuclear foci. Here, we aimed to validate the RAD51 score cut off and compare the performance of this test to other HRR deficiency (HRD) detection methods. Laboratory models from BRCA1/BRCA2-associated breast cancer, HGSOC, and PaC were developed and evaluated for their response to PARPi and cisplatin. HRD in these models and patient samples was evaluated by DNA sequencing of HRR genes, genomic HRD tests, and RAD51 foci detection. We established patient-derived xenograft models from breast cancer (n = 103), HGSOC (n = 4), and PaC (n = 2) that recapitulated patient HRD status and treatment response. The RAD51 test showed higher accuracy than HRR gene mutations and genomic HRD analysis for predicting PARPi response (95%, 67%, and 71%, respectively). RAD51 detection captured dynamic changes in HRR status upon acquisition of PARPi resistance. The accuracy of the RAD51 test was similar to HRR gene mutations for predicting platinum response. The predefined RAD51 score cut off was validated, and the high predictive value of the RAD51 test in preclinical models was confirmed. These results collectively support pursuing clinical assessment of the RAD51 test in patient samples from randomized trials testing PARPi or platinum-based therapies. SIGNIFICANCE: This work demonstrates the high accuracy of a histopathology-based test based on the detection of RAD51 nuclear foci in predicting response to PARPi and cisplatin.

High FGFR1-4 mRNA Expression Levels Correlate with Response to Selective FGFR Inhibitors in Breast Cancer.

PURPOSE: FGFR1 amplification (FGFR1amp) is recurrent in metastatic breast cancer (MBC) and is associated with resistance to endocrine therapy and CDK4/6 inhibitors (CDK4/6is). Multi-tyrosine kinase inhibitors (MTKIs) and selective pan-FGFR inhibitors (FGFRis) are being developed for FGFR1amp breast cancer. High-level FGFR amplification and protein expression by IHC have identified breast cancer responders to FGFRis or MTKIs, respectively. EXPERIMENTAL DESIGN: Here, we used preclinical models and patient samples to identify predictive biomarkers to these drugs. We evaluated the antitumor activity of an FGFRi and an MTKI in a collection of 17 breast cancer patient-derived xenografts (PDXs) harboring amplification in FGFR1/2/3/4 and in 10 patients receiving either an FGFRi/MTKI. mRNA levels were measured on FFPE tumor samples using two commercial strategies. Proliferation and angiogenesis were evaluated by detecting Ki-67 and CD31 in viable areas by immunofluorescence. RESULTS: High FGFR1-4 mRNA levels but not copy-number alteration (CNA) is associated with FGFRi response. Treatment with MTKIs showed higher response rates than with FGFRis (86% vs. 53%), regardless of the FGFR1-4 mRNA levels. FGFR-addicted PDXs exhibited an antiproliferative response to either FGFRis or MTKIs, and PDXs exclusively sensitive to MTKI exhibited an additional antiangiogenic response. Consistently, the clinical benefit of MTKIs was not associated with high FGFR1-4 mRNA levels and was observed in patients previously treated with antiangiogenic drugs. CONCLUSIONS: Tailored therapy with FGFRis in molecularly selected MBC based on high FGFR1-4 mRNA levels warrants prospective validation in patients with CDK4/6i-resistant luminal breast cancer and in patients with TNBC without targeted therapeutic options.

Phase I prognostic online (PIPO): A web tool to improve patient selection for oncology early phase clinical trials.

PURPOSE: Patient selection in phase 1 clinical trials (Ph1t) continues to be a challenge. The aim of this study was to develop a user-friendly prognostic calculator for predicting overall survival (OS) outcomes in patients to be included in Ph1t with immune checkpoint inhibitors (ICIs) or targeted agents (TAs) based on clinical parameters assessed at baseline. METHODS: Using a training cohort with consecutive patients from the VHIO phase 1 unit, we constructed a prognostic model to predict median OS (mOS) as a primary endpoint and 3-month (3m) OS rate as a secondary endpoint. The model was validated in an internal cohort after temporal data splitting and represented as a web application. RESULTS: We recruited 799 patients (training and validation sets, 558 and 241, respectively). Median follow-up was 21.2 months (m), mOS was 10.2 m (95% CI, 9.3-12.7) for ICIs cohort and 7.7 m (95% CI, 6.6-8.6) for TAs cohort. In the multivariable analysis, six prognostic variables were independently associated with OS - ECOG, number of metastatic sites, presence of liver metastases, derived neutrophils/(leukocytes minus neutrophils) ratio [dNLR], albumin and lactate dehydrogenase (LDH) levels. The phase 1 prognostic online (PIPO) calculator showed adequate discrimination and calibration performance for OS, with C-statistics of 0.71 (95% CI 0.64-0.78) in the validation set. The overall accuracy of the model for 3m OS prediction was 87.2% (95% CI 85%-90%). CONCLUSIONS: PIPO is a user-friendly objective and interactive tool to calculate specific survival probabilities for each patient before enrolment in a Ph1t. The tool is available at https://pipo.vhio.net/.

PI3K activation promotes resistance to eribulin in HER2-negative breast cancer.

BACKGROUND: Eribulin is a microtubule-targeting agent approved for the treatment of advanced or metastatic breast cancer (BC) previously treated with anthracycline- and taxane-based regimens. PIK3CA mutation is associated with worse response to chemotherapy in oestrogen receptor-positive (ER+)/human epidermal growth factor receptor 2-negative (HER2-) metastatic BC. We aimed to evaluate the role of phosphoinositide 3-kinase (PI3K)/AKT pathway mutations in eribulin resistance. METHODS: Resistance to eribulin was evaluated in HER2- BC cell lines and patient-derived tumour xenografts, and correlated with a mutation in the PI3K/AKT pathway. RESULTS: Eleven out of 23 HER2- BC xenografts treated with eribulin exhibited disease progression. No correlation with ER status was detected. Among the resistant models, 64% carried mutations in PIK3CA, PIK3R1 or AKT1, but only 17% among the sensitive xenografts (P = 0.036). We observed that eribulin treatment induced AKT phosphorylation in vitro and in patient tumours. In agreement, the addition of PI3K inhibitors reversed primary and acquired resistance to eribulin in xenograft models, regardless of the genetic alterations in PI3K/AKT pathway or ER status. Mechanistically, PI3K blockade reduced p21 levels likely enabling apoptosis, thus sensitising to eribulin treatment. CONCLUSIONS: PI3K pathway activation induces primary resistance or early adaptation to eribulin, supporting the combination of PI3K inhibitors and eribulin for the treatment of HER2- BC patients.

OncoTree: A Cancer Classification System for Precision Oncology.

PURPOSE: Cancer classification is foundational for patient care and oncology research. Systems such as International Classification of Diseases for Oncology (ICD-O), Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT), and National Cancer Institute Thesaurus (NCIt) provide large sets of cancer classification terminologies but they lack a dynamic modernized cancer classification platform that addresses the fast-evolving needs in clinical reporting of genomic sequencing results and associated oncology research. METHODS: To meet these needs, we have developed OncoTree, an open-source cancer classification system. It is maintained by a cross-institutional committee of oncologists, pathologists, scientists, and engineers, accessible via an open-source Web user interface and an application programming interface. RESULTS: OncoTree currently includes 868 tumor types across 32 organ sites. OncoTree has been adopted as the tumor classification system for American Association for Cancer Research (AACR) Project Genomics Evidence Neoplasia Information Exchange (GENIE), a large genomic and clinical data-sharing consortium, and for clinical molecular testing efforts at Memorial Sloan Kettering Cancer Center and Dana-Farber Cancer Institute. It is also used by precision oncology tools such as OncoKB and cBioPortal for Cancer Genomics. CONCLUSION: OncoTree is a dynamic and flexible community-driven cancer classification platform encompassing rare and common cancers that provides clinically relevant and appropriately granular cancer classification for clinical decision support systems and oncology research.

A CT-based Radiomics Signature Is Associated with Response to Immune Checkpoint Inhibitors in Advanced Solid Tumors.

Background Reliable predictive imaging markers of response to immune checkpoint inhibitors are needed. Purpose To develop and validate a pretreatment CT-based radiomics signature to predict response to immune checkpoint inhibitors in advanced solid tumors. Materials and Methods In this retrospective study, a radiomics signature was developed in patients with advanced solid tumors (including breast, cervix, gastrointestinal) treated with anti-programmed cell death-1 or programmed cell death ligand-1 monotherapy from August 2012 to May 2018 (cohort 1). This was tested in patients with bladder and lung cancer (cohorts 2 and 3). Radiomics variables were extracted from all metastases delineated at pretreatment CT and selected by using an elastic-net model. A regression model combined radiomics and clinical variables with response as the end point. Biologic validation of the radiomics score with RNA profiling of cytotoxic cells (cohort 4) was assessed with Mann-Whitney analysis. Results The radiomics signature was developed in 85 patients (cohort 1: mean age, 58 years ± 13 [standard deviation]; 43 men) and tested on 46 patients (cohort 2: mean age, 70 years ± 12; 37 men) and 47 patients (cohort 3: mean age, 64 years ± 11; 40 men). Biologic validation was performed in a further cohort of 20 patients (cohort 4: mean age, 60 years ± 13; 14 men). The radiomics signature was associated with clinical response to immune checkpoint inhibitors (area under the curve [AUC], 0.70; 95% CI: 0.64, 0.77; P < .001). In cohorts 2 and 3, the AUC was 0.67 (95% CI: 0.58, 0.76) and 0.67 (95% CI: 0.56, 0.77; P < .001), respectively. A radiomics-clinical signature (including baseline albumin level and lymphocyte count) improved on radiomics-only performance (AUC, 0.74 [95% CI: 0.63, 0.84; P < .001]; Akaike information criterion, 107.00 and 109.90, respectively). Conclusion A pretreatment CT-based radiomics signature is associated with response to immune checkpoint inhibitors, likely reflecting the tumor immunophenotype. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Summers in this issue.

Evolving Landscape of Molecular Prescreening Strategies for Oncology Early Clinical Trials.

Most academic precision oncology programs have been designed to facilitate enrollment of patients in early clinical trials with matched targeted agents. Over the last decade, major changes were seen both in the targetable molecular alteration landscape and in drug development trends. In this article, we describe how the Vall d'Hebron Institute of Oncology molecular prescreening program adapted to a dynamic model of biomarker-drug codevelopment. We started with a tumor-agnostic hotspot mutation panel plus in situ hybridization and immunohistochemistry of selected markers and subsequently transitioned to tumor-specific amplicon-based next-generation sequencing (NGS) tests together with custom copy number, fusion, and outlier gene expression panels. All assays are optimized for archived formalin-fixed paraffin-embedded tumor tissues without matched germline sequencing. In parallel, biomarker-matched trials evolved from a scenario of few targets and large populations (such as PI3K inhibitors in PIK3CA mutants) to a complex situation with many targets and small populations (such as multiple targetable fusion events). Recruitment rates in clinical trials with mandatory biomarkers decreased over the last 3 years. Molecular tumor board meetings proved critical to guide oncologists on emerging biomarkers for clinical testing and interpretation of NGS results. The substantial increase of immunotherapy trials had a major impact in target prioritization and guided clinical implementation of new markers, such as tumor mutational burden, with larger exon-based NGS assays and gene expression signatures to capture microenvironment infiltration patterns. This new multiomics era of precision oncology is expected to increase the opportunities for early clinical trial matching.

Capturing Hyperprogressive Disease with Immune-Checkpoint Inhibitors Using RECIST 1.1 Criteria.

PURPOSE: Most hyperprogression disease (HPD) definitions are based on tumor growth rate (TGR). However, there is still no consensus on how to evaluate this phenomenon. PATIENTS AND METHODS: We investigated two independent cohorts of patients with advanced solid tumors treated in phase I trials with (i) programmed cell death 1 (PD-1)/PD-L1 antibodies in monotherapy or combination and (ii) targeted agents (TA) in unapproved indications. A Response Evaluation Criteria in Solid Tumors (RECIST) 1.1-based definition of hyperprogression was developed. The primary endpoint was the assessment of the rate of HPD in patients treated with ICIs or TAs using both criteria (RECIST and TGR) and the impact on overall survival (OS) in patients who achieved PD as best response. RESULTS: Among 270 evaluable patients treated with PD-1/PD-L1 inhibitors, 29 PD-1/PD-L1-treated patients (10.7%) had HPD by RECIST definition. This group had a significantly lower OS (median of 5.23 months; 95% CI, 3.97-6.45) when compared with the non-HPD progressor group (median, 7.33 months; 95% CI, 4.53-10.12; HR = 1.73, 95% CI, 1.05-2.85; P = 0.04). In a subset of 221 evaluable patients, 14 (6.3%) were categorized as HPD using TGR criteria, differences in median OS (mOS) between this group (mOS 4.2 months; 95% IC, 2.07-6.33) and non-HPD progressors (n = 44) by TGR criteria (mOS 6.27 months; 95% CI, 3.88-8.67) were not statistically significant (HR 1.4, 95% IC, 0.70-2.77; P = 0.346). Among 239 evaluable patients treated with TAs, 26 (10.9%) were classified as having HPD by RECIST and 14 using TGR criteria in a subset of patients. No differences in OS were observed between HPD and non-HPD progressors treated with TAs. CONCLUSIONS: HPD measured by TGR or by RECIST was observed in both cohorts of patients; however, in our series, there was an impact on survival only in the immune-checkpoint inhibitor cohort when evaluated by RECIST. We propose a new way to capture HPD using RECIST criteria that is intuitive and easy to use in daily clinical practice.

A RAD51 assay feasible in routine tumor samples calls PARP inhibitor response beyond BRCA mutation.

Poly(ADP-ribose) polymerase (PARP) inhibitors (PARPi) are effective in cancers with defective homologous recombination DNA repair (HRR), including BRCA1/2-related cancers. A test to identify additional HRR-deficient tumors will help to extend their use in new indications. We evaluated the activity of the PARPi olaparib in patient-derived tumor xenografts (PDXs) from breast cancer (BC) patients and investigated mechanisms of sensitivity through exome sequencing, BRCA1 promoter methylation analysis, and immunostaining of HRR proteins, including RAD51 nuclear foci. In an independent BC PDX panel, the predictive capacity of the RAD51 score and the homologous recombination deficiency (HRD) score were compared. To examine the clinical feasibility of the RAD51 assay, we scored archival breast tumor samples, including PALB2-related hereditary cancers. The RAD51 score was highly discriminative of PARPi sensitivity versus PARPi resistance in BC PDXs and outperformed the genomic test. In clinical samples, all PALB2-related tumors were classified as HRR-deficient by the RAD51 score. The functional biomarker RAD51 enables the identification of PARPi-sensitive BC and broadens the population who may benefit from this therapy beyond BRCA1/2-related cancers.

Analysis of mutant allele fractions in driver genes in colorectal cancer - biological and clinical insights.

Sequencing of tumors is now routine and guides personalized cancer therapy. Mutant allele fractions (MAFs, or the 'mutation dose') of a driver gene may reveal the genomic structure of tumors and influence response to targeted therapies. We performed a comprehensive analysis of MAFs of driver alterations in unpaired primary and metastatic colorectal cancer (CRC) at our institution from 2010 to 2015 and studied their potential clinical relevance. Of 763 CRC samples, 622 had detailed annotation on overall survival in the metastatic setting (OSmet) and 89 received targeted agents matched to KRAS (MEK inhibitors), BRAF (BRAF inhibitors), or PIK3CA mutations (PI3K pathway inhibitors). MAFs of each variant were normalized for tumor purity in the sample (adjMAFs). We found lower adjMAFs for BRAFV600E and PIK3CA than for KRAS, NRAS, and BRAF non-V600 variants. TP53 and BRAFV600E adjMAFs were higher in metastases as compared to primary tumors, and high KRAS adjMAFs were found in CRC metastases of patients with KRAS wild-type primary tumors previously exposed to EGFR antibodies. Patients with RAS- or BRAFV600E -mutated tumors, irrespective of adjMAFs, had worse OSmet. There was no significant association between adjMAFs and time to progression on targeted therapies matched to KRAS, BRAF, or PIK3CA mutations, potentially related to the limited antitumor activity of the employed drugs (overall response rate of 4.5%). In conclusion, the lower BRAFV600E and PIK3CA adjMAFs in subsets of primary CRC tumors indicate subclonality of these driver genes. Differences in adjMAFs between metastases and primary tumors suggest that approved therapies may result in selection of BRAFV600E - and KRAS-resistant clones and an increase in genomic heterogeneity with acquired TP53 alterations. Despite significant differences in prognosis according to mutations in driver oncogenes, adjMAFs levels did not impact on survival and did not help predict benefit with matched targeted agents in the metastatic setting.