Terry Hyslop

<jats:title>Abstract</jats:title> <jats:p>Background: MRI measurements (Li et al., Magn Reson Imaging 2019; Hylton et al., Radiology 2016) and ctDNA (Magbanua et al., SABCS 2018) have both been independently shown to associate with response to NAT. We performed a retrospective study to examine correlation between ctDNA and MRI and to investigate whether information from these two measurements can be combined to improve early prediction of response.</jats:p> <jats:p>Methods: We analyzed serial ctDNA and MRI data from 84 high-risk (stage II/III) breast cancer patients collected at baseline (T0), 3 weeks after initiation of paclitaxel-based NAT (T1), between paclitaxel and anthracycline regimens (T2), and after NAT prior to surgery (T3). The response variable was pathologic complete response (pCR), defined as the absence of invasive tumor in the breast and lymph nodes after NAT. We examined correlations between MR functional tumor volume (FTV) and ctDNA using Spearman's rho (r). Mean FTV between ctDNA+/- groups were compared using t-test. Monte Carlo simulation was used to assess correlation between FTV and ctDNA trajectories in individual patients. We investigated the impact of adding ctDNA information to MR FTV-based predictors using receiver operating characteristic curves to calculate area under the curve (AUC), logistic regressions, and decision trees using recursive partitioning.</jats:p> <jats:p>Results: The mean levels of ctDNA (mutant molecules/mL plasma) were significantly correlated with FTV at all timepoints [T0 (r=0.49), T1 (r=0.42), T2 (r=0.42), T3 (r=0.43), all p&amp;lt;0.05]. The mean FTV in patients who had detectable ctDNA was significantly higher compared to those who were negative (all timepoints, all p&amp;lt;0.05). FTV and ctDNA trajectories in individual patients over the course of therapy were correlated (empirical 1-sided p=0.046). Adding continuous ctDNA information (mutant molecules/mL plasma) to FTV at T1 improved AUC in the pCR-prediction model, but the increase was not statistically significant (FTV: 0.59, FTV+ctDNA: 0.69, p=0.25). No improvements in AUCs were observed at other timepoints. Treated as a dichotomous variable, ctDNA positivity at T1 trended toward association with non-pCR in logistic regression models at T2 and T3 with MR-based prediction scores as a covariate (0.05).</jats:p> <jats:p>Citation Format: Mark Jesus M. Magbanua, Laura H. Hendrix, Terry Hyslop, William T. Barry, Eric P. Winer, Clifford Hudis, Deborah Toppmeyer, Lisa Anne Carey, Ann H. Partridge, Jean-Yves Pierga, Tanja Fehm, José Vidal-Martínez, Dimitrios Mavroudis, Jose A. Garcia-Saenz, Justin Stebbing, Paola Gazzaniga, Luis Manso, Rita Zamarchi, María Luisa Antelo, Leticia De Mattos-Arruda, Daniele Generali, Carlos Caldas, Elisabetta Munzone, Luc Dirix, Amy L. Delson, Harold Burstein, Misbah Qadir, Cynthia Ma, Janet H. Scott, François-Clément Bidard, John W. Park, Hope S. Rugo. Circulating tumor DNA (ctDNA) and magnetic resonance imaging (MRI) for monitoring and predicting response to neoadjuvant therapy (NAT) in high-risk early breast cancer patients in the I-SPY 2 TRIAL [abstract]. In: Proceedings of the AACR Special Conference on Advances in Liquid Biopsies; Jan 13-16, 2020; Miami, FL. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(11_Suppl):Abstract nr A50.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>Cancer proteogenomics combines genomics, transcriptomics and mass spectrometry-based proteomics to gain insights into cancer biology and treatment responsiveness. While proteogenomics analyses have already shown great potential to deepen our understanding of cancer tissue complexity and signaling, how a patient’s tumor changes upon treatment has largely been the province of genomics. This is due to technical difficulties associated with doing proteogenomic analysis on clinic-derived core-needle biopsies. To address this critical need, we have developed a “microscaled” proteogenomics approach for tumor-rich OCT-embedded core needle biopsies. Tissue-sparing specimen processing (“Biopsy Trifecta EXTraction”, BioTExt) and microscaled proteomics (MiProt) methodologies allowed generation of deep-scale proteogenomics datasets, with copy number and transcript information for &amp;gt;20,000 genes and mass spectrometry-based identification and quantification of nearly all expressed proteins in a tumor (&amp;gt;10,000 proteins) and more than &amp;gt;20,000 phosphosites starting with just 25 micrograms of protein per sample. In order to understand the capabilities and limitations our our approach relative to more conventional deepscale proteomics requiring &amp;gt;10X more starting material, we compared preclinical patient derived xenograft (PDX) models at conventional scale with data obtained by core-needle biopsy of the same tissues. Comparable depth and biological insights were obtained from the cores relative to surgically resected tumors. As a proof-of-concept for implementation in clinical trials, we applied microscaled proteogenomic methods to a small-scale clinical study where biopsies were accrued from patients with ERBB2+ locally advanced breast cancer before and 48 to 72 hours after the first dose of neoadjuvant Trastuzumab-based chemotherapy. Multi-omics comparisons were conducted between samples associated with residual disease versus samples associated with complete pathological response. Integrative, microscaled proteogenomic analyses efficiently diagnosed the molecular bases of diverse candidate treatment resistance mechanisms including: 1) absence of ERBB2 amplification (false-ERBB2+); 2) insufficient ERBB2 activity for therapeutic sensitivity despite ERBB2 amplification (pseudo-ERBB2+); 3) resistance features in true-ERBB2+ cases including androgen receptor signaling, mucin expression and an inactive immune microenvironment; 4) lack of acute phospho-ERBB2 down-regulation in non-pCR cases. In summary, we have developed a robust proteogenomics pipeline well suited for large-scale cancer clinical studies to identify potential resistance mechanism in patients. We conclude that microscaled cancer proteogenomics could improve diagnostic precision in the clinical setting.</jats:p> <jats:p>Citation Format: Shankha Satpathy, Eric Jaehnig, Krug Karsten, Beom-Jun Kim, Alexander Saltzman, Doug Chan, Kimberly Holloway, Meenakshi Anurag, Chen Huang, Purba Singh, Ari Gao, Noel Namai, Yongchao Dou, Bo Wen, Suhas Vasaikar, David Mutch, Mark Watson, Cynthia Ma, Foluso Ademuyiwa, Mothaffar Rimawi, Jeremy Hoog, Samuel Jacobs, Anna Malovannaya, Terry Hyslop, D.R Mani, Charles Perou, George Miles, Bing Zhang, Michael Gillette, Steven Carr, Matthew Ellis. Microscaled proteogenomic methods for precision oncology [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr GS2-05.</jats:p>

BACKGROUND: We examined the prognostic significance of circulating tumor cell (CTC) dynamics during treatment in metastatic breast cancer (MBC) patients receiving first-line chemotherapy. METHODS: Serial CTC data from 469 patients (2202 samples) were used to build a novel latent mixture model to identify groups with similar CTC trajectory (tCTC) patterns during the course of treatment. Cox regression was used to estimate hazard ratios for progression-free survival (PFS) and overall survival (OS) in groups based on baseline CTCs, combined CTC status at baseline to the end of cycle 1, and tCTC. Akaike information criterion was used to select the model that best predicted PFS and OS. RESULTS: Latent mixture modeling revealed 4 distinct tCTC patterns: undetectable CTCs (56.9% ), low (23.7%), intermediate (14.5%), or high (4.9%). Patients with low, intermediate, and high tCTC patterns had statistically significant inferior PFS and OS compared with those with undetectable CTCs (P < .001). Akaike Information Criterion indicated that the tCTC model best predicted PFS and OS compared with baseline CTCs and combined CTC status at baseline to the end of cycle 1 models. Validation studies in an independent cohort of 1856 MBC patients confirmed these findings. Further validation using only a single pretreatment CTC measurement confirmed prognostic performance of the tCTC model. CONCLUSIONS: We identified 4 novel prognostic groups in MBC based on similarities in tCTC patterns during chemotherapy. Prognostic groups included patients with very poor outcome (intermediate + high CTCs, 19.4%) who could benefit from more effective treatment. Our novel prognostic classification approach may be used for fine-tuning of CTC-based risk stratification strategies to guide future prospective clinical trials in MBC.

<jats:title>Abstract</jats:title> <jats:p>Background: Triple-negative breast cancer is an aggressive disease with limited treatment options beyond chemotherapy. In CALGB 40603, adding either carboplatin (Cb) or bevacizumab (Bev) to standard neoadjuvant chemotherapy (NACT) increased pathologic complete response (pCR) rates in TNBC and the subset of Basal-like breast cancers. Transcriptomic studies have previously found that evidence of immune activation was significantly associated with pCR. We are now examining frequency and pCR impact of germline and somatic mutations as secondary analyses.</jats:p> <jats:p>Methods: 281 pretreatment tumors and matched blood samples were sequenced using a 1,123 cancer-associated gene panel. Germline and somatic mutations were determined and somatic mutations were further evaluated and filtered using a previously identified list of likely false positives (Bailey, Cell 2019) and RNA expression of the variant. We examined mutation frequency and association of mutations with overall in-breast pCR (result available for 274 of our cohort) and for benefit of addition of Cb or Bev on pCR.</jats:p> <jats:p>Results: The pCR rate in patients with DNA sequencing was 53.6% in TNBC (147 pCR, 127 non-pCR) and 53.3% in Basal-like (131 pCR, 115 non-pCR). Total number of somatic mutations per sample from our targeted cancer panel ranged from one to 1,041, with a median of 18 mutations per patient. Ten samples had more than 100 mutations, of which six had POLE or POLD1 mutations. Focusing on non-silent coding mutations reduced the number of variants in the entire cohort from 10,483 to 4,275. A second filter was used to remove likely false positives. Mutations in TP53 were identified in 90% of samples. Only two other genes - KMT2C (12%) and MACF1 (11%) - were mutated in more than 10% of the samples. Mutations were also identified in NF1 (8%), PIK3CA (8%), PTEN (7%), FAT1 (7%), and RB1 (7%). Somatic alterations in BRCA1 and BRCA2 were observed in 6% and 5% of samples, respectfully. We also found that 40% of the tumors had one or more mutations in a set of 18 histone modification genes. However, these mutations were not associated with pCR in the entire cohort or in the Basal-like subset (n=246). The mutations were also not associated with response to Cb or Bev. The germline was also analyzed for inherited variants. We identified 35 (12%) patients with pathogenic or likely pathogenic variants in BRCA1 (n=23), BRCA2 (n=6), PALB2 (n=3), ATM (n=2), and CHEK2 (n=1). Germline variants were not associated with pCR in the entire cohort (n=33) or Basal subset (n=32), but there was a trend toward higher pCR in germline carriers (n=15) versus non-germline carriers (n=123) within the Bev-treated cohort (80% vs 54%, p=0.09). The pCR rate in patients with a pathogenic germline or somatic BRCA1/2 mutation (n=55) was similar to that for the overall study population (53% pCR rate), as was the increase in pCR seen with the addition of Cb (61% vs. 41% pCR rate) (Sikov, JCO 2015).</jats:p> <jats:p>Conclusions: Analysis of recurrently mutated genes in TNBC among patients treated in CALGB 40603 revealed high rates of mutation and very high TP53 mutation frequency but failed to identify mutations significantly associated with pCR rate. Integrated analyses with copy number, immune and other RNA features are underway. Support for the project came from U10CA180821, U10CA180882; BCRF, Susan G. Komen, and Genentech. https://acknowledgments.alliancefound.org</jats:p> <jats:p>ClinicalTrials.gov Identifier: NCT00861705</jats:p> <jats:p>Citation Format: Katherine A Hoadley, Bradford C. Powell, Dona Kanavy, David Marron, Lisle E. Mose, Terry Hyslop, Donald A. Berry, Olwen Hahn, Sara M. Tolaney, William M. Sikov, Charles M. Perou, Lisa A. Carey. Mutational analysis of triple-negative breast cancer (TNBC): CALGB 40603 (Alliance) [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P4-05-03.</jats:p>