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

Breast cancer; Cancer models; Predictive markersCáncer de mama; Modelos de cáncer; Marcadores predictivosCàncer de pulmó; Models de càncer; Marcadors predictiusCDK4/6 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

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

Immunotherapy; Phase 1 trials; Prognostic modelInmunoterapia; Ensayos de fase 1; Modelo pronósticoImmunoteràpia; Assajos de fase 1; Model pronòsticPurpose 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/.The research leading to these results has received funding from “la Caixa” Foundation (LCF/PR/CE07/50610001). Cellex Foundation for providing research facilities and equipment. This work was supported by the Accelerator Award (UpSMART) from Fundacion Científica – Asociacion Espanola Contra el Cancer (FC -AECC)/ Associazione Italiana per la Ricerca sul Cancro (AIRC) /Cancer Research United Kingdom (CRUK)

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

Colorectal cancer; Driver gene; Mutant allele fractionCàncer colorectal; Gen conductor; Fracció d'al·lel mutantCáncer colorrectal; Gen conductor; Fracción de alelo mutanteSequencing 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

Evolving Landscape of Molecular Prescreening Strategies for Oncology Early Clinical Trials

Oncologia de precisió; Assaigs clínics; Preselecció molecularOncología de precisión; Ensayos clínicos; Preselección molecularPrecision oncology; Clinical Trials; Molecular prescreeningMost 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