Citations

TPS535 Background: Small bowel adenocarcinoma (SBA) is a rare malignancy, possibly because of the high rate of immunosurveillance in baseline small bowel function, and is frequently diagnosed in late stages. Due to these factors, there is no consistent standard of care for metastatic disease beyond first-line where several prospective studies have confirmed activity of FOLFOX/CAPOX. Historically, second-line treatment of SBA has been extrapolated from colon/gastric cancer. Recent studies suggest that SBA has a distinct genomic profile, including higher incidence of microsatellite instability, compared to gastric and colon cancers. Additionally, the tumor microenvironment reflects a greater degree of PD-L1 expression and infiltration by lymphocytes. Immune checkpoint inhibitors have not previously been tested in SBA unselected for MSI status. Methods: The study is a phase 2, multicenter, single-arm clinical trial of pembrolizumab (200 mg IV every 3 weeks) for patients with unresectable or metastatic SBA refractory to first-line chemotherapy. Forty patients with any tumor mismatch repair status will be enrolled at 8 US sites as part of the ACCRU network and International Rare Cancer Initiative (IRCI) with a primary objective to assess the confirmed response rate (H0 p < 10%, HA: p > 30%) to pembrolizumab. Secondary endpoints include PFS, OS, and adverse events. Correlative aims include assessing blood and tissue biomarkers (i.e. PD-L1, MSI-H/MSS status, cfDNA, mutation burden, etc.) for association with clinical benefit. An interim analysis will be performed to assess efficacy after 18 patients become evaluable. The study is open with 16 patients enrolled at time of submission. Clinical trial information: NCT02949219.

Abstract Taselisib is a potent and selective tumor growth inhibitor through PI3K pathway suppression. Thirty-four patients with locally advanced or metastatic solid tumors were treated (phase I study, modified 3+3 dose escalation; 5 cohorts; 3–16 mg taselisib once-daily capsule). Taselisib pharmacokinetics were dose-proportional; mean half-life was 40 hours. Frequent dose-dependent, treatment-related adverse events included diarrhea, hyperglycemia, decreased appetite, nausea, rash, stomatitis, and vomiting. At 12 and 16 mg dose levels, dose-limiting toxicities (DLT) were observed, with an accumulation of higher-grade adverse events after the cycle 1 DLT assessment window. Pharmacodynamic findings showed pathway inhibition at ≥3 mg in patient tumor samples, consistent with preclinical PIK3CA-mutant tumor xenograft models. Confirmed response rate was 36% for PIK3CA-mutant tumor patients with measurable disease [5/14: 4 breast cancer (3 patients at 12 mg); 1 non–small cell lung cancer], where responses started at 3 mg, and 0% in patients with tumors without known PIK3CA hotspot mutations (0/15). Significance: Preliminary data consistent with preclinical data indicate increased antitumor activity of taselisib in patients with PIK3CA-mutant tumors (in comparison with patients with tumors without known activating PIK3CA hotspot mutations) starting at the lowest dose tested of 3 mg, thereby supporting higher potency for taselisib against PIK3CA-mutant tumors. Cancer Discov; 7(7); 704–15. ©2017 AACR. See related commentary by Rodon and Tabernero, p. 666. This article is highlighted in the In This Issue feature, p. 653

ABSTRACTNext‐generation sequencing technologies have afforded unprecedented characterization of low‐frequency and rare genetic variation. Due to low power for single‐variant testing, aggregative methods are commonly used to combine observed rare variation within a single gene. Causal variation may also aggregate across multiple genes within relevant biomolecular pathways. Kernel‐machine regression and adaptive testing methods for aggregative rare‐variant association testing have been demonstrated to be powerful approaches for pathway‐level analysis, although these methods tend to be computationally intensive at high‐variant dimensionality and require access to complete data. An additional analytical issue in scans of large pathway definition sets is multiple testing correction. Gene set definitions may exhibit substantial genic overlap, and the impact of the resultant correlation in test statistics on Type I error rate control for large agnostic gene set scans has not been fully explored. Herein, we first outline a statistical strategy for aggregative rare‐variant analysis using component gene‐level linear kernel score test summary statistics as well as derive simple estimators of the effective number of tests for family‐wise error rate control. We then conduct extensive simulation studies to characterize the behavior of our approach relative to direct application of kernel and adaptive methods under a variety of conditions. We also apply our method to two case‐control studies, respectively, evaluating rare variation in hereditary prostate cancer and schizophrenia. Finally, we provide open‐source R code for public use to facilitate easy application of our methods to existing rare‐variant analysis results.

Lung cancer is the deadliest cancer in the United States, killing roughly one of four cancer patients in 2016. While it is well-established that lung cancer is caused primarily by environmental effects (particularly tobacco smoking), there is evidence for genetic susceptibility. Lung cancer has been shown to aggregate in families, and segregation analyses have hypothesized a major susceptibility locus for the disease. Genetic association studies have provided strong evidence for common risk variants of small-to-moderate effect. Rare and highly penetrant alleles have been identified by linkage studies, including on 6q23–25. Though not common, some germline mutations have also been identified via sequencing studies. Ongoing genomics studies aim to identify additional high penetrance germline susceptibility alleles for this deadly disease.

ABSTRACTRare variants (RVs) have been shown to be significant contributors to complex disease risk. By definition, these variants have very low minor allele frequencies and traditional single‐marker methods for statistical analysis are underpowered for typical sequencing study sample sizes. Multimarker burden‐type approaches attempt to identify aggregation of RVs across case‐control status by analyzing relatively small partitions of the genome, such as genes. However, it is generally the case that the aggregative measure would be a mixture of causal and neutral variants, and these omnibus tests do not directly provide any indication of which RVs may be driving a given association. Recently, Bayesian variable selection approaches have been proposed to identify RV associations from a large set of RVs under consideration. Although these approaches have been shown to be powerful at detecting associations at the RV level, there are often computational limitations on the total quantity of RVs under consideration and compromises are necessary for large‐scale application. Here, we propose a computationally efficient alternative formulation of this method using a probit regression approach specifically capable of simultaneously analyzing hundreds to thousands of RVs. We evaluate our approach to detect causal variation on simulated data and examine sensitivity and specificity in instances of high RV dimensionality as well as apply it to pathway‐level RV analysis results from a prostate cancer (PC) risk case‐control sequencing study. Finally, we discuss potential extensions and future directions of this work.

Abstract Background: Preclinical and clinical data indicate a key role for the PI3-kinase (PI3K) pathway in the pathogenesis of resistance to endocrine therapies in hormone receptor-positive (HR) breast cancer (BC) and suggest that combining PI3K inhibitors with endocrine therapy may partially overcome this resistance. FERGI is the first randomized Phase II study testing pictilisib (GDC-0941), a PI3K inhibitor, in combination with fulvestrant to evaluate this hypothesis in MBC patients with and without PIK3CA-mutant tumors. Methods: 168 post-menopausal pts with ER-positive, HER2-negative MBC were randomized (1:1) to receive fulvestrant with either pictilisib 340 mg QD (n=89, "combination" arm) or matching placebo (n=79, "control" arm). To be eligible, pts had to have relapsed during or within 6 mos of completing adjuvant AI treatment or have progressed on an AI for MBC. Pts were stratified based on tumor PIK3CA mutation status, resistance to prior AI therapy and presence of measurable disease. The primary endpoint was PFS by investigator assessment in the intent-to-treat (ITT) group and in pts with centrally confirmed PIK3CA-mutant tumors. The primary analysis was based on a 6 mo median duration follow up. Results: Baseline disease and prior treatment characteristics were similar between study arms. Observed treatment-emergent AEs were consistent with those previously described for single agent pictilisib and fulvestrant (primary toxicities were rash and GI disorders). In the ITT population (84 events) the median PFS (mPFS) was 6.2 mo in the combination arm vs 3.8 months for the control arm (HR, 0.77; 95% CI, 0.50-1.19). For pts with PIK3CA-mutant tumors (37 events), mPFS was 6.2 mo in the combination arm vs 5.1 mo in the control arm (HR, 0.92; 95% CI, 0.48-1.76). For pts without a detectable PIK3CA mutant tumor (43 events), mPFS was 5.8 months in the combination arm vs 3.6 months in the control arm (HR, 0.64; 95% CI, 0.35-1.17). Exploratory post-hoc subgroup analysis suggested improvement in PFS in pts with ER+ and PR+ tumors (centrally confirmed) treated with pictilisib plus fulvestrant. In the ER+/PR+ subgroup (57 events) mPFS was 7.2 mo in the combination arm vs 3.7 mo in the control arm (HR, 0.46; 95% CI, 0.27 to 0.78). This improvement was independent of tumor PIK3CA mutation status. Multivariate analysis suggests that this treatment effect in pts with ER+/PR+ tumors is maintained after adjusting for possible baseline imbalances. A similar analysis on pts with luminal A tumors (per PAM50 analysis) was also consistent with the findings in pts ER+/PR+ disease. Conclusions: This is the first report of a blinded, randomized clinical study evaluating a PI3K inhibitor in pts with MBC. In the ITT population, the addition of pictilisib to fulvestrant was associated with a mPFS improvement of 3.8 mo to 6.2 mo. Exploratory subgroup analyses suggested in pts with ER+/PR+ tumors are more likely to derive benefit from the addition of pictilisib to fulvestrant irrespective of PIK3CA mutation status, though the subgroup analyses are limited by the sample size. Additional biomarker analyses will be reported. Citation Format: Ian Krop, Stephen Johnston, Ingrid A Mayer, Maura Dickler, Vinod Ganju, Andres Forero-Torres, Bohuslav Melichar, Serafin Morales, Richard de Boer, Steven Gendreau, Mika Derynck, Mark Lackner, Jill Spoerke, Ru-Fang Yeh, Gallia Levy, Vivian Ng, Carol O'Brien, Heidi Savage, Yuanyuan Xiao, Timothy Wilson, Soo Chin Lee, Katarina Petrakova, Susanne Vallentin, Denise Yardley, Matthew Ellis, Martine Piccart, Edith A Perez, Eric Winer, Peter Schmid. The FERGI phase II study of the PI3K inhibitor pictilisib (GDC-0941) plus fulvestrant vs fulvestrant plus placebo in patients with ER+, aromatase inhibitor (AI)-resistant advanced or metastatic breast cancer – Part I results [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr S2-02.

Abstract Background: Estrogen receptors are over-expressed in around 70% of breast cancer cases. The genetic changes that occur during aromatase inhibitor (AI) treatment are not well understood and may differ depending upon the patient's response phenotype. Methods: We performed whole genome sequencing (WGS) of matched blood, pre-treatment, and post-treatment biopsy samples from 22 estrogen receptor positive breast cancer patients treated with neoadjuvant aromatase inhibitors. For 5 cases, we performed the whole genome sequencing (WGS) on patients’ matched normal, two pre AI-treatment, and two post AI-treatment DNA isolates from biopsy samples. We validated all putative coding and non-coding somatic mutations using deep sequencing. By comparing the validated somatic mutations from pre- and post- AI treatment biopsy samples, we were able to determine the alterations in the tumor genomes. In every case we defined the clonal architecture of each pair of pre-treatment and post-treatment biopsy samples by comparing the variant allele frequencies from thousands of validated somatic mutations. Results: Comparisons of the two pre AI-treatment biopsy samples from the same patient indicates that the variant allele frequencies of mutations showed high concordances in all 5 cases, 0.74 to 0.95 range of correlation coefficient. Only a small percentage of somatic mutations were detected in one pre-treatment sample and not the other (4.65% overall). In comparing the somatic variations between pre-treatment and matched post-treatment biopsy samples in 22 cases, we found that patients with good clinical response to AI treatment retained known driver mutations only in their pre-treatment tumors. Conversely, those patients with poor clinical response presented new driver mutations in their post-treatment samples. Furthermore, the variant allele frequency for most mutated genes decreased in post AI treatment samples for patients with good AI treatment response; on the contrary, the variant allele frequency increased for patients with poor clinical response. Conclusions: From WGS of matched normal, pre-treatment, and post-treatment biopsy samples, we identified new driver genes mutated in patients with poor clinical response, while patients with good clinical response had lost mutated driver genes in their post-treatment biopsy samples. The genetic landscape revealed by WGS of pre-treatment and post-treatment biopsy samples reveals mutational repertoires are remodeled by AI therapy. This finding suggests deep sequencing of AI treated samples will be necessary to reveal the complete complement of mutations present in a patient's tumor. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr LB-423. doi:1538-7445.AM2012-LB-423