Radiologic and Genomic Evolution of Individual Metastases during HER2 Blockade in Colorectal Cancer

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1 Article Radiologic and Genomic Evolution of Individual Metastases during HER2 Blockade in Colorectal Cancer Graphical Abstract Authors Giulia Siravegna, Luca Lazzari, Giovanni Crisafulli,..., Salvatore Siena, Silvia Marsoni, Alberto Bardelli Correspondence In Brief Siravegna et al. identify genetic events associated with ERBB2 amplified metastatic colorectal cancers resistant to trastuzumab plus lapatinib treatment and reveal lesion-private evolutionary patterns. Analyses of metastases from a patient unveil metastasis-specific evolution and pharmacologic vulnerabilities. Highlights d ERBB2, RAS, PIK3CA mutations are associated with resistance to HER2 blockade in mcrc d d d A liquid biopsy test would have identified >85% primary resistance cases Lesion size and contribution to plasma ctdna were correlated Patterns of lesion-specific mutations and TCR were longitudinally compared in blood Siravegna et al., 2018, Cancer Cell 34, July 9, 2018 ª 2018 Elsevier Inc.

2 Cancer Cell Article Radiologic and Genomic Evolution of Individual Metastases during HER2 Blockade in Colorectal Cancer Giulia Siravegna, 1,9 Luca Lazzari, 1,2,3,9 Giovanni Crisafulli, 1,3,9 Andrea Sartore-Bianchi, 4 Benedetta Mussolin, 1 Andrea Cassingena, 4 Cosimo Martino, 1 Richard B. Lanman, 5 Rebecca J. Nagy, 5 Stephen Fairclough, 5 Giuseppe Rospo, 1 Giorgio Corti, 1 Alice Bartolini, 1 Pamela Arcella, 1 Monica Montone, 1 Francesca Lodi, 3 Annalisa Lorenzato, 3 Alice Vanzati, 4 Emanuele Valtorta, 4 Giovanni Cappello, 1 Andrea Bertotti, 1,3 Sara Lonardi, 6 Vittorina Zagonel, 6 Francesco Leone, 1,3 Mariangela Russo, 1 Antonella Balsamo, 1 Mauro Truini, 4 Federica Di Nicolantonio, 1,3 Alessio Amatu, 4 Erica Bonazzina, 4 Silvia Ghezzi, 4 Daniele Regge, 1 Angelo Vanzulli, 4,7 Livio Trusolino, 1,3,8 Salvatore Siena, 4,7,8 Silvia Marsoni, 1,2,4,8 and Alberto Bardelli 1,3,8,10, 1 Candiolo Cancer Institute-FPO, IRCCS, Candiolo, TO, Italy 2 FIRC Institute of Molecular Oncology (IFOM), Milan, Italy 3 Department of Oncology, University of Torino, Candiolo, TO 10060, Italy 4 Niguarda Cancer Center, Grande Ospedale Metropolitano Niguarda, Milan 20162, Italy 5 Guardant Health, Redwood City, CA 94063, USA 6 Istituto Oncologico Veneto - IRCCS, Oncologia Medica 1, Padova 35128, Italy 7 Department of Oncology and Hemato-Oncology, Università degli Studi di Milano, Milan 20122, Italy 8 Co-senior author 9 These authors contributed equally 10 Lead Contact Correspondence: alberto.bardelli@unito.it SUMMARY Targeting HER2 is effective in 24% of ERBB2 amplified metastatic colorectal cancer; however, secondary resistance occurs in most of the cases. We studied the evolution of individual metastases during treatment to discover spatially resolved determinants of resistance. Circulating tumor DNA (ctdna) analysis identified alterations associated with resistance in the majority of refractory patients. ctdna profiles and lesion-specific radiographic reports revealed organ- or metastasis-private evolutionary patterns. When radiologic assessments documented progressive disease in target lesions, response to HER2 blockade was retained in other metastases. Genomic and functional analyses on samples and cell models from eight metastases of a patient co-recruited to a postmortem study unveiled lesion-specific evolutionary trees and pharmacologic vulnerabilities. Lesion size and contribution of distinct metastases to plasma ctdna were correlated. INTRODUCTION Functional and molecular profiles of colorectal cancers (CRCs) revealed relatively few actionable targets (Bertotti et al., 2015). Despite extensive genomic analysis, the only targeted agents in clinical use for metastatic CRC (mcrc) are the antibodies cetuximab and panitumumab, which inhibit the epidermal growth factor receptor (EGFR) (Bardelli and Siena, 2010). Using xenografts derived from hepatic metastases of CRC patients, we previously identified amplification of ERBB2, encoding HER2, as an effective therapeutic target in cetuximab-resistant tumors (Bertotti et al., 2011). Patient-derived mcrc xenografts with amplified ERBB2 were sensitive to a dual HER2-blockade with the monoclonal antibody trastuzumab in combination with the small Significance The evolution of individual metastases during treatment in combination with cell-free circulating tumor DNA (ctdna) analysis enables the identification of alterations associated with resistance in most of the patients clinically refractory to HER2 blockade. We revealed organ- and metastases-private evolutionary patterns as well as a high level of intra-patient molecular heterogeneity, delineating lesion-specific evolutionary trees and pharmacologic vulnerabilities. Using a patient- and lesionspecific next-generation DNA sequencing panel approach, lesion size and the individual contribution of each metastasis to plasma ctdna were correlated. 148 Cancer Cell 34, , July 9, 2018 ª 2018 Elsevier Inc.

3 molecule EGFR/HER2 inhibitor lapatinib, but not to either agent alone. These preclinical data provided a strong rationale for clinical targeting of ERBB2 amplified mcrc. Hence, we designed HERACLES, a phase II trial aimed at testing trastuzumab and lapatinib in patients with ERBB2 amplified CRC (Sartore-Bianchi et al., 2016; Siena et al., 2017) refractory to standard-of-care treatments, including cetuximab or panitumumab. In total 33 patients were enrolled and evaluable for response. Notably, complete responses (CRs) were observed in two patients (6.1%) and partial responses (PRs) in six patients (18.2%), giving an objective response rate of 24.2%, with durable clinical benefit lasting >24 weeks in 13 of 33 cases (Sartore-Bianchi et al., 2016). Patients with higher ERBB2 copy number (R9.6) in tumor tissues had significantly longer time to progression (TTP; median 26.6 versus 13.4 weeks; p < 0.001) and longer overall survival (median 53.1 versus 34.0 weeks; p < 0.01) than those with low ERBB2 gene copy number (<9.6) (Siena et al., 2017). Dual HER2 blockade with trastuzumab and lapatinib was well tolerated and severe toxicities were not observed (Siena et al., 2017). Although HER2 blockade was remarkably effective, especially considering that the patients had received a median of five prior lines of treatment, most cases eventually relapsed, with a median progression-free survival (PFS) of 21 weeks (Sartore- Bianchi et al., 2016). How ERBB2-amplified gastrointestinal tumors that initially respond to HER2 blockade evolve during therapy and become resistant is largely unknown (Kim et al., 2018; Owen et al., 2018). Further knowledge is key to devising additional lines of treatment or mounting combinatorial preventive strategies. RESULTS Molecular Landscapes Predictive of Response and Primary Resistance to HER2 Blockade in the Blood of HERACLES Patients To uncover molecular mechanisms of resistance to dual HER2 blockade, we exploited blood samples collected during the HERACLES trial of 30 out of 35 patients. Overall during the study, two patients had achieved CR, six had PR, 15 had stable disease (SD), and seven had not benefited from the therapy, showing progressive disease (PD) as best response (Figures 1A, 1B, and S1A). As per study design, blood samples were obtained from all patients prior to initiation of treatment (baseline), every 15 days during therapy, and at radiological progression or at the end of treatment, whichever came first. Next-generation DNA sequencing (NGS) analysis was performed on 52 banked plasma samples from 29 of the 30 patients (96.6%) using a targeted 73-gene circulating tumor DNA (ctdna) NGS panel (Figures S1A and S1B). Twenty-nine of the 52 samples were collected at baseline and 23 were collected at progression. In patients where clinical benefit (PR and SD) was followed by relapse, ctdna profiles obtained at progression and baseline were compared, while for PD cases only the baseline ctdna samples were analyzed (Figure 1C and Table S1). Genetic alterations in ctdna were found in 50 of 52 samples and revealed the presence of KRAS, NRAS, BRAF, PIK3CA, MAP2K1, ERBB2, EGFR, and MET mutations and/or copy number changes (Figure 1C and Table S1). ERBB2 copy number alteration (CNA) was confirmed in plasma in 51 of the 52 samples for a sensitivity of 97.9% (95% confidence interval [CI], 87.2% 99.8%) and a positive predictive value of 100% (95% CI, 90.4% 100%). Alterations in RAS/RAF were detected at baseline in 6 of 7 (86%) refractory patients but only in 3 of 22 (14%) cases with clinical benefit, highlighting the importance of the MAPK pathway as key mediator of resistance to anti-her2 therapies. Among patients who had experienced clinical benefit, emerging KRAS mutant clones and BRAF amplification were identified at progression in two SD patients and one PR case, respectively (Figure 1C). Other alterations detected at progression involved ERBB2, EGFR, PIK3CA, and PTEN (Figure 1C), suggesting an involvement of the PI3K-AKT pathway in the acquisition of resistance to dual HER2 blockade. Among such alterations, PIK3CA mutations and ERBB2 p.l755s and p.v777l have already been linked to resistance to trastuzumab and/or lapatinib treatment in breast cancer (Bose et al., 2013; Hanker et al., 2013; Kancha et al., 2011; Loibl et al., 2014, 2016; Xu et al., 2017). Clonality analysis revealed that mutations expected to confer resistance could be broadly classified in two groups according to the variant allele frequencies (VAFs), likely reflecting primary and secondary resistance mechanisms (Figure 1D and Table S1). In most patients, alterations that appeared only at progression had VAFs lower than those of early-occurring genetic alterations (hereafter referred as trunk mutations ), suggesting they were sub-clonal. Accordingly, mutations that were revealed in the baseline blood draws had higher VAFs, indicating their clonal origin (Figure 1D and Table S1). These results suggest that a comprehensive liquid biopsy interventional test would have been more useful than tissue-based HER2 status as the sole determinant of eligibility of patients for the HERACLES therapy, as it would have likely identified more than 85% of those who had shown primary resistance when treated with lapatinib and trastuzumab. While the functional impact of most of the singlenucleotide variants (SNVs) we identified has been extensively validated (for example, the KRAS or BRAF hotspot mutations), a few variants (such as EGFR p.i918l and ERBB2 p.n1193s) are not reported in databases. To test the impact of these variants we applied a mutation assessor software (Reva et al., 2011) and found that both changes have a predicted neutral impact on the activity of the two receptors. The patients in whom these variants were found ( and ) responded to HERACLES treatment, suggesting that these alterations are unlikely to be involved in resistance to HER2 blockade. Longitudinal Tracking of Resistant Clones in the Blood of HERACLES Patients To retrospectively track tumor clonal dynamics in cases where we identified putative mechanisms of resistance, we performed longitudinal plasma ctdna analysis of samples collected during treatment with dual HER2-blockade concomitantly with computed tomography (CT) scan evaluations (Figures 2 and S2, Table S2, and Figure S3). Besides ERBB2 amplification, other driver genetic events, such as APC or TP53 mutations, were also monitored longitudinally (Figures 2 and S2). The dynamics of APC, TP53, or ERBB2 alleles throughout the therapy paralleled clinical response, decreasing while patients were responding, and increasing at disease progression. Cancer Cell 34, , July 9,

4 A B % TTP (months) % max tumor shrinkage 50% 0% -50% PD SD SD - PR unconfirmed PR/CR C -100% APC CNA > 5 copies TP53 CNA 3-5 copies ERBB2 CNA < 3 copies EGFR MUTATED KRAS UPON PROGRESSION NRAS BRAF MAP2K PIK3CA PTEN MET No ctdna available No alterations found at progression D Relative clonality APC TP53 KRAS NRAS BRAF RAF1 PIK3CA PR SD PD PTEN ERBB2 EGFR EGFR Figure 1. Liquid Biopsies to Monitor Response and to Identify Mechanisms of Resistance to Trastuzumab and Lapatinib Treatment (A C) Waterfall plot representing time to progression (TTP) (A), bar graph showing maximum tumor shrinkage (B), and oncoprint summary of molecular alterations identified by next-generation sequencing (NGS) of plasma ctdna (C) of the 30 patients included in the study. In (B), asterisks indicate patients who were declared to have progressive disease by clinical evaluation without CT scans available at progression, whereas dotted red lines indicate RECIST 1.1 cutoffs. In (B), for primary resistance patients and the one with ongoing therapy (PD and CR, respectively) only the baseline time point (prior to treatment) was considered, while for secondary resistance cases (SD and PR) the baseline blood draw was compared with the one obtained at progression. In (C), dark blue-framed boxes highlight molecular alterations acquired at resistance to trastuzumab and lapatinib treatment. (D) Clonality status of genetic alterations found in blood and expected to be associated with resistance to trastuzumab and lapatinib, normalized on driver events in selected 16 patients. See also Figure S1 and Table S1. Similarly, trunk mutant alleles associated to primary resistance in PD and SD patients (e.g., KRAS in patient and BRAF mutations in patient ) paralleled clinical response as markers of tumor burden during treatment course. Instead, mutant subclones associated with acquired resistance (e.g., PIK3CA mutations in patients and ) emerged at later time points during therapy, when clinical progression was defined (Figures 1D, 2, and S2). 150 Cancer Cell 34, , July 9, 2018

5 Shrinkage TUMOR RESPONSE Growth % mutated alleles APC R499 PIK3CA E545K KRAS A146T ERBB2 CNA BL PD/EXIT trastuzumab+lapatinib treatment (days) CNA PM01 PM02 PM03 PM04 PM05 PM06 PM07 PM08 PM09 PM10 PM11 PM12 LM01 LM02 LM03 LM04 LM05 LM06 LM07 LM08 LM09 LM10 LM11 BL-0 SD-69 PD % mutated alleles ERBB2 CNA KRAS G12A RAF1 S257L KRAS Q61H AC EGFR CNA PDGFRA R293H PIK3CA E545A C NA PM01 PM02 PM03 PM04 PM05 PM06 PM07 PM08 LM01 LM02 LM03 LM04 LM05 LM06 LM07 BL-0 SD-53 SD-83 SD-168 SD-225 PD-251 % m utated alleles PIK3CA or ERBB2 % mutatedalleles BL PD PD/EXIT trastuzumab+lapatinib treatment (days) TP53 R175H ERBB2 CNA PIK3CA H1047R BL PR PD PD/EXIT trastuzumab+lapatinib treatment (days) ERBB2 CNA NOTCH1 G2436fs ERBB2 L755S PIK3CA E545K BL PD PD/EXIT trastuzumab+lapatinib treatment (days) CNA % mu ta ted alle les PM01 PM02 PM03 PM04 PM05 PM06 PM07 PM08 PM09 PM10 PM11 PM12 PM13 PM14 PM15 PM16 PM17 PM18 PM19 PM20 LM01 LM02 LM03 LM04 LM05 LM06 LM07 LM08 LM09 CNA or NOTCH1 BL-0 PR-62 PM01 PM02 PM03 PM04 PM05 PM06 PM07 PM08 PM09 PM10 PM11 PM12 PM13 PM14 LM01 LM02 LM03 LM04 LM05 LM06 LM07 LM08 PR-118 BL-0 PR-174 SD-58 PR-230 SD-114 PR-293 SD-177 PD-402 PD-240 (legend on next page) Cancer Cell 34, , July 9,

6 Metastasis-Specific Patterns of Radiologic Response and Resistance to HER2 Blockade During the HERACLES trial, responses and tumor progressions were assessed according to standard RECIST 1.1 criteria (Eisenhauer et al., 2009). Hence, only a maximum of five target lesions were identified at baseline for outcome assessment, irrespective of the number of lesions visible in the patient, and monitored at regular time points during the course of treatment until disease progression documented by RECIST; i.e., > 20% increase in sum of target lesions. However, with the advent of precision medicine, these criteria are increasingly felt to be unfit to capture the evolution of tumors under targeted treatment. The molecular heterogeneity observed with liquid biopsies prompted us to retrospectively re-evaluate all lesions to see whether the genomic heterogeneity captured in blood was also mirrored by imaging. We therefore selected 10 cases (Figures 2 and S3) and collected all CT scans performed during treatment. This analysis revealed several instances of mixed radiologic response at early time points, as shown by the representative cases depicted in Figure 2. Indeed, while some lesions enlarged under treatment, supporting the overall clinical judgment of PD, others decreased in size or completely disappeared. Next, we integrated ctdna profiles with radiological assessments: this revealed that, while non-responding patients already had high VAF of mutant resistance genes at baseline (indicative of clonal driver events), in responding patients resistant alleles emerged later, were typically sub-clonal, and anticipated the time of imaging-assessed progression by a mean of 60 days. Longitudinal radiological assessment during HER2 blockade unveiled lesion-specific response in almost all cases (Figure S3). Of note, we found that in some instances patterns of progression were anatomically restricted, being preferentially circumscribed to certain organs. For example, in case , almost every lung metastasis progressed, while the hepatic lesions shrank and remarkably disappeared (Figures 2 and S3). In cases , , and , all lesions showed progression at the last CT assessment. Molecular Landscapes of Individual Metastases during HER2 Blockade To characterize the molecular heterogeneity associated with response of individual metastases to HER2 blockade we exploited DONUM, a warm autopsy protocol (NCT ) that enables rapid processing of postmortem tissue samples. Patient received HER2 blockade with lapatinib and trastuzumab as third line of treatment for 5.5 months, obtaining a tumor shrinkage of the target lesions that was classified as SD according to RECIST 1.1 criteria (unconfirmed partial response; Figures 1A and 1B). After therapy cessation, ERBB2 ctdna levels remained elevated and the patient underwent HER2 targeted monotherapy with trastuzumab-emtansine (T-DM1) as part of the HERACLES-RESCUE protocol (NCT ), which failed to control the disease, followed by the antiangiogenic multi-kinase inhibitor regorafenib until further progression and death (Figures 3A, S3, and S4A). Radiological analysis conducted during the entire clinical course revealed a homogeneous pattern of response among different lesions during first-line therapy (FOLFIRI plus cetuximab and FOLFOX plus bevacizumab). In contrast, during third-line therapy with trastuzumab and lapatinib (Figure 3A), case displayed a more marked heterogeneity of lesion-specific patterns of response: three out of eight liver metastases (LMs: LM01, LM02, and LM04) rapidly progressed, while the other five shrank (Figures 3A, 3B, and S3). This is consistent with what was documented for other HERACLES patients treated with dual anti-her2 therapy. Patient (hereafter referred to as DONUM002) consented to the DONUM study. Eight distinct hepatic lesions were collected under viable conditions immediately after exitus and each one was subdivided in up to four fragments depending on their size; each sub-lesion was then processed to obtain formalin-fixed paraffin-embedded (FFPE) blocks, and two fragments of each sub-lesion were engrafted in non-obese diabetic (NOD) severe combined immunodeficiency (SCID) mice to obtain patient-derived xenografts (PDXs). The remaining materials were frozen and bio-banked for further analysis (Figure S4A). Immunohistochemistry (IHC) analysis on FFPE blocks of each sub-lesion revealed inter- and intra-lesion heterogeneous pattern of HER2 expression. LM02 showed no HER2 expression; LM03, LM06, LM07, and LM08 showed high levels of HER2 expression, while LM04 showed low levels of HER2 expression (Figure S4B). Heterogeneous HER2 protein expression levels paralleled ERBB2 amplification, as detected in the sub-lesions via fluorescence in situ hybridization (FISH) analysis (Figure S4B). HER2 amplification was further validated by droplet digital PCR (ddpcr)-based genetic analysis on genomic DNA isolated from each sub-lesion (Figure S4C). Interestingly, increased EGFR protein expression levels were identified in focal regions of LM04, and FISH confirmed a heterogeneous pattern of EGFR amplification in this lesion (Figure S4B). Comparative assessment of HER2 protein overexpression or gene amplification and radiological evaluation of individual metastases revealed that five out of six HER2-positive lesions responded, while two out of three lesions that did not respond to treatment were HER2 negative (Figures 4 and S4B), consistent with the notion that ERBB2 amplification and overexpression confer dependency on HER2 signals. Figure 2. Longitudinal Tracking of Resistance and Radiologic Patterns of Response to Dual HER2 Blockade in HERACLES Patients The left graph of each case shows longitudinal, liquid biopsy-based ctdna analyses in the indicated patient showing ERRB2 copy number amplifications (light blue bars) over time during trastuzumab and lapatinib treatment. Colored lines indicate clonal evolution of genomic variations occurring at drug resistance as assessed by ctdna analysis every 15 days of treatment. The middle table of each case shows lesion-specific radiographic response during trastuzumab and lapatinib treatment as percentage variations of largest diameter of individual lesions from nadir. Asterisks indicate the target lesions used for RECIST 1.1 evaluation during the HERACLES trial. BL, baseline; PD, progressive disease; LM, liver metastases; PM, pulmonary metastases. Gray boxes indicate baseline CT scan measurement; dotted boxes indicate lesions that were not present when evaluated at the indicated time point; slashed boxes indicate not evaluable lesion. The picture on the right of each case shows anatomic localization of metastatic lesions. Blue and red dots represent tumor lesions responding and progressing at the end of HER2 treatment, respectively, as assessed by CT scans. White dots represent lesions that completely disappeared during treatment. See also Figures S2 and S3 and Table S Cancer Cell 34, , July 9, 2018

7 A B Lesion s ize (% of baseline tr eatment) FOLFIRI + cetuximab FOLFOX + beva HERACLES lapatinib + TM AB Phylogenetic Analysis of Metastatic Dissemination CRCs are thought to develop through progressive clonal sweeps associated with acquisition of somatic mutations. Molecular alterations that occur early are present in every sub-clone, indicating that there must have been a single ancestral cell; all mutations that occurred later are instead sub-clonal (branched mutations). We set out to determine the evolutionary phylogenetic trees of metastatic dissemination for patient DONUM002. To this end, we performed whole-exome sequencing (WES) analysis on DNA obtained from the FFPE primary tumor samples and from each of the eight LMs as well as on the germinal DNA obtained from patient s peripheral blood mononuclear cells (PBMCs). To identify intra-lesion clonal variations, multiregional spatial sequencing was performed for five metastases (LM01, LM03, LM04, LM05, LM08), analyzing each sub-fragment in which these lesions were further divided. In total, WES was performed on 20 independent samples. WES data were used to reconstruct the evolutionary trees from the primary tumor to T-DM1 rego LM01 LM02 LM03 LM04 LM05 LM06 LM07 LM08 Figure 3. Lesion-Specific Evolution to Dual HER2 Blockade in Patient (A) Bar graph showing lesion-specific response to the indicated therapies. Each bar represents lesion size as a percentage of baseline treatment (prior to any specific treatment). (B) Representative CT scans of two out of eight liver lesions assessed during HERACLES treatment. Lesion 4 rapidly progressed to trastuzumab + lapatinib treatment while lesion 8 markedly responded. See also Figures S3 and S4. the LMs (Figures 4 and S4). To define tree structures, we considered each metastasis as being composed of geographically (topologically) distinct independent clones that spawned from the primary tumor. Overall the data show that individual lesions followed distinct evolutionary trajectories as outlined in Figure 4. For example, LM04 (resistant to therapy) followed a star-like evolution, giving rise to highly heterogeneous sublesions that likely differentiated early during disease dissemination. The phylogenetic distance among the LM08 sub-lesions (responding to therapy) indicate an asparagus-like evolution pattern, likely reflecting the fact that this sub-lesion differentiated in more recent times (Figure 4). WES data of the eight hepatic lesions and derivative sub-lesion fragments were then used to define the molecular landscape of metastatic dissemination in DONUM002. WES analysis showed a total of 4,030 SNVs (Figures 5A and S4A, Tables S3 and S4), of which 335 were identified in the primary tumor; 35.2% (118 of 335) of SNVs were detected in all metastatic sites and were defined as truncal alterations, including nonsense APC p.y1376 and dominant-negative TP53 p.r273h mutations. Interestingly, 38.2% (128 of 335) of SNVs were present in only some of the metastatic sites, suggesting molecular divergence among metastatic lesions during the course of the disease. Instead, 26.6% (89 of 335) of SNVs were found to be primary tumor private, as they were not found in the metastases (Table S3). Comparison of cross-lesion SNVs revealed that metastases were not genetically homogeneous, with the exception of metastasis 6 and 8, which were highly related at the molecular level. Specifically, we identified a subset of highly related lesions (p < ), including LM06, LM08 (all sub-lesions), and LM01 sub-lesion 3 (LM01-03); private mutations of these fragments had lower VAFs (from 2.98% of LM08-03 to 7.24% in LM01-03). The remaining sub-lesions were clearly more heterogeneous, displaying a higher number of private mutations and relative VAFs (from 17.55% of LM05-02 to 54.99% in LM03-02) (Figure 5A and Table S3). LM06 and Cancer Cell 34, , July 9,

8 ERBB2 amplified EGFR amplified Figure 4. Molecular Heterogeneity of Individual Metastatic Deposits in One Patient Graphic representation of the eight individual liver lesions (LM01 to LM08) from case gathered through the rapid autopsy protocol. Blue and red lesions represent responding and progressing sites, respectively, as assessed by radiological imaging at the end of anti-her2 treatment. Depending of the lesion size, up to four different sublesions were characterized from each metastatic deposit. The number of each individual sub-lesion is indicated. The phylogenetic tree showing the evolutionary distance of each lesion from the primary tumor is listed. For lesions LM01, LM04, LM05, and LM08, high-resolution trees showing evolutionary distance within each sub-lesion are also depicted. ERBB2 and EGFR amplified regions are indicated in orange and purple, respectively. See also Figure S4. T+L, trastuzumab + lapatinib. LM08 were highly related at the molecular level, sharing 603 of 630 (95.7% LM06) and 603 of 741 (81.4% LM08) alterations, including the same ERBB2 p.v777l mutation (Figure 5A and Table S3). Altogether these data suggest that the evolutionary paths of LM06 and LM08 branched later as compared with the other metastases (Figures 4 and 5A). Similarities were also observed between LM07 and LM03: although 210 shared alterations were identified between these two lesions, only 83 of 210 were exclusively shared also by sub-lesion LM03-01, while only 11 of 210 were exclusively shared by LM03-02 (Figure 5A). Gene copy number profiles by WES data confirmed an inter- and intra-lesion heterogeneous pattern of ERBB2 amplification, and EGFR amplification, in LM04-04, which we comfirmed by FISH and IHC analysis. Mutational Burden, Neoantigen Load, and T Cell Clonality in Distinct Metastatic Lesions The tumor mutational burden (TMB) and the neoantigen load (NAL) for each sub-lesion were calculated based on somatic variations and RNA sequencing (RNA-seq) values. TMB values of LM06 and LM08 were higher than those of other metastases (average TMB LM06/LM08 = 16.8 Mut/MB versus LMs = 11.8 Mut/MB, p < 0.01) (Figure 5B). Similarly, the number of predicted neoantigens in LM06/LM08 was greater than in the other lesions (average LM6/LM8 = 125 NALs versus other LMs = 65 NALs, p < 0.01) (Figure 5C). Differences in TMB and NAL between LM06/LM08 and the other metastases were not associated with a hypermutator status since clinically relevant mutations in mismatch repair-related genes were not identified and the microsatellite stable status of each sub-lesion was confirmed by NGS data and PCR-based assays (Table S4). We further investigated inter- and intra-lesion heterogeneity of the adaptive immune component. Using an NGS immunose- Responding lesion to T+L Progressing lesion to T+L quencing approach that assesses the extent and distribution of multiple and single T cell clones in the tumor tissue, we evaluated the T cell receptor (TCR) variable b chain (TCR-Vb) CDR3 regions as a measure of T cell clonality in the same DNA samples used for mutational analysis. CD3 + TCR amount varied among lesions and within sub-lesions (i.e., LM04 and LM05), highlighting a heterogeneous pattern of immune infiltration. Most TCRs were polyclonal, as heterogeneous TCR expansion patterns were identified in each sub-lesion (Figure 5D). ctdna Profiles Reveal Metastasis-Specific Patterns of Response and Relapse We reasoned that the availability of detailed genomic maps of the eight lesions could allow determining the contribution of individual metastases to the ctdna pool at multiple time points during treatment. We therefore designed a lesion-specific SNV amplicon assay and sequenced a total of 19 plasma samples collected during treatment (Figure 6 and Table S5). Three types of SNVs were studied: truncal SNVs (homozygous and heterozygous alterations), which were present in the primary tumor and in all lesions, were selected to determine the tumor content of each sample. Eight SNVs of clinically relevant oncogenic alleles (i.e., ERBB2 p.v777l and PTEN p.k223del7) and 25 SNVs that were exclusively present in each lesion but absent in the primary tumor were also included (Tables S5 and S6). To calculate the limit of detection (LOD) of the amplicon assay, a mix of genomic DNA from tissues of the eight lesions was sequenced together with two unrelated plasma samples, serving as negative controls. According to this assessment, the LOD of the panel was 0.35% (see STAR Methods and Table S6). Results obtained by plasma ctdna sequencing were then compared with radiological measurements of individual lesions. As shown in Figure 6, tumor burden assessed in ctdna using truncal alterations (i.e., APC and TP53) correlated with clinical response. Most notably, a linear correlation between lesion size and plasma ctdna-specific release emerged (R = 0.88, 154 Cancer Cell 34, , July 9, 2018

9 Figure 5. Evolutionary Trajectories and Metastatic Dissemination in Patient (A) Genomic similarities among sub-lesions identified by hierarchical clustering (top) and heatmap showing number of shared, common, and private mutations of all sub-lesions with associated VAF (bottom). Colored lines identifying individual metastases are correlated with clinical response during HERACLES treatment: red, non-responding; blue, responding. Amplification of ERBB2 and EGFR are indicated in orange and purple, respectively. High VAF are depicted in red, low VAF in blue (minimum VAF, 1%). WT, wild-type. (B) Tumor mutational burden (TMB; expressed as the number of mutations per megabase) for each sub-lesion and the primary tumor as in (A). (C) NAL expressed as the absolute number of predicted neoantigens detected in individual metastases as in (A). For each metastasis, the numbers of shared neoantigens among all the sublesions are indicated with color scale from blue (private neoantigens) to red (shared neoantigens). (D) Violin plots showing the distribution and density probability of the 20 most-represented TCR templates sequenced in each sub-lesion as in (A). The shape width represents the frequency of TCR rearrangements at each y level. Interquartile ranges and the 95% CIs are represented by the thick and thin bars, respectively; the median value is indicated by the white dot. See also Figure S4 and Tables S3 and S4. N.A., not available. p<10 9 by Pearson and R = 0.72, p < by Spearman nonparametric rank) (Figure S5A). Lesions LM05 and LM06 remained stable during the whole treatment and this was paralleled by constant shedding of tumor DNA into the circulation (Figures 6B 6E). LM04, which did not respond to trastuzumab and lapatinib treatment, or to T-DM1 or to regorafenib administration, released high levels of ctdna. When considering the normalized tumor burden on the total ctdna content, the smallest lesion (LM02, less than 2 cm) released the smallest quantity of DNA in the blood. Remarkably, however, ctdna fragments from LM02 could be reliably identified in early blood draws (from July 2015 to January 2016). Inversely, LM08 was found to release the maximum amount of ctdna in early time points, while its specific content decreased during therapy, paralleling the progressively reduced size of this metastasis during response to treatments (Figure 6). The genomic differences among the sub-lesions allowed us to evaluate the specific ctdna contribution of different sub-fragments to the pool of total plasma ctdna and to correlate this observation with the portion of metastases that had progressed during treat- ment. Indeed, while the patient was clinically progressing (starting April 2016), the ctdna content related to LM03 mainly originated from the LM03-01 sub-lesion, while the LM03-02 ctdna content remained stable or decreased. Based on linear correlation (Figure S5A), we can assume that a given VAF corresponds to a lesion s size, suggesting asymmetrical response to therapy of LM03 sub-lesions. Dynamics of Tumor-Infiltrating Lymphocytes in Metastatic Lesions and Peripheral Blood during HER2 Therapy We hypothesized that the availability of lesion-specific ctdna assay and TCR profiles could be combined to track the evolution of cancer and immune cells during treatment. We asked whether and to what extent the repertoire of TCR clonotypes might reflect tumor evolution during the emergence of therapeutic resistance. To monitor the evolution of TCR profiles, we analyzed PBMCs serially collected prior to and during HER2 blockade, at progression, and on subsequent therapies until death. Accordingly, we performed TCR sequencing (TCR-seq) in 19 blood time points that were collected in parallel to those in which ctdna analysis was previously performed. We observed that the total number of productive TCRs was higher at baseline, diminished at the time of treatment initiation, increased at tumor shrinkage and at clinical response, and decreased again at tumor progression Cancer Cell 34, , July 9,

10 156 Cancer Cell 34, , July 9, 2018 (legend on next page)

11 until exitus. Notably, TCR levels were anti-correlated with ctdna release identified in the matched ctdna time points (R = 0.81, p < 0.05, Figures S5B, 6E, and 6F), where the specific ctdna tumor content was calculated to be 4.9% or higher of the total cellfree DNA pool. While ctdna release paralleled the evolution of individual metastases during therapy, longitudinal TCR analysis was not associated with the behavior of distinct lesions but displayed a homogeneous pattern over time (Figure 6F). Pharmaco-Genomic Analysis of Metastases-Specific Cell Models To functionally and mechanistically characterize the biological properties of individual metastases, we generated lesion (and sub-lesion)-specific cell models from tissue fragments obtained during the autopsy of patient DONUM002. To this end, we engrafted tissue fragments in immune-deficient mice; upon tumor growth, 2D cell cultures were derived from each lesion and whenever possible from individual sub-lesions. This process resulted in the establishment of six independent cell models derived from five different lesions (Figure 7). To assess whether individual cell lines reflected the molecular characteristics of the lesion from which they derived we performed genomic and molecular characterization. The molecular profiles of individual sub-lesions were largely maintained in the derivative cell models. Specifically, ERRB2 and EGFR CNA evaluated by ddpcr in matched tissues and derived cell lines showed a high level of correlation (R = by Pearson, p < ; Figures S4C, 7A, and 7B). Heterogeneous expression of HER2 measured by IHC in the metastatic sub-lesions (Figure S4B) translated into differential HER2 protein expression among the corresponding cell lines, as assessed by immunofluorescence and western blot analyses (Figures 7C and 7D). High levels of HER2 were detected in LM07-01A cells, while its expression was barely detectable in cells derived from LM02-01A. EGFR amplification revealed by WES analysis in LM04-04 sub-lesion was paralleled by high protein expression in the matched cell line (LM04-04A), while EGFR expression was low in the other cellular models. Similarly, an LM05-02-derivative cell line did not express PTEN protein, likely reflecting the PTEN p.k223del7 alteration detected by WES analysis (VAF 60%) in the matched lesion. We previously reported that some CRC cell lines are highly sensitive to cetuximab even in presence of low EGFR expression (Medico et al., 2015; Misale et al., 2014). As discussed above, only some of the lesions responded to the HERACLES treatment, while genomic and IHC analyses revealed heterogeneous HER2 profiles. To gather functional insights in these findings we subjected all cell models to lapatinib and trastuzumab combinatorial treatment. A mixed response emerged, which closely paralleled the clinical outcome of the lesions from which the models were derived: cells from HER2- positive metastases responded, while those derived from nonresponder lesions displayed almost complete refractoriness to the drugs (Figure 7E). Low levels of HER2 protein (or complete lack of expression) in the LM02-01A-derived cell line was associated with unresponsiveness to anti-her2 blockade. Interestingly, high levels of EGFR expression in LM04-04A were associated with resistance to lapatinib and trastuzumab treatment. In contrast, cells derived from a different portion of lesion LM04 (LM04-01B) were instead markedly sensitive to HER2 blockade. While highly genomically concordant with LM04-04A cells, the LM04-01B model did not express high levels of EGFR, thus highlighting EGFR amplification and overexpression as a mechanism of resistance to anti-her2 therapy. Treatment of patient DONUM002 involved administration of the immune-conjugate trastuzumab-emtansine (T-DM1) as part of the HERACLES-RESCUE protocol. Unfortunately, T-DM1 was ineffective and the patient rapidly progressed during this regimen (Figures 3 and 6A 6C). To functionally characterize the lack of T-DM1 activity, cell models derived from this case were deployed. Modest or no efficacy was observed in the majority of the cells with T-DM1. EGFR blockade was also attempted as cetuximab monotherapy, but this also was ineffective (Figure 7F). Based on molecular findings, we hypothesized that preventing ligand-mediated activation of both HER2 and EGFR might overcome lack of response to HERACLES therapy in this patient. Remarkably, combination of T-DM1 and cetuximab restricted the growth of the lines derived from the majority of the metastases and was significantly more effective than T-DM1 or cetuximab alone (Figure 7F). DISCUSSION Assessment of response to treatment in individuals with solid cancers is presently based on the RECIST 1.1 criteria (Eisenhauer et al., 2009). According to RECIST guidelines, up to a maximum of two lesions per organ and five lesions in total are identified as target lesions and recorded. Target lesions are selected according to their size (lesions with the longest diameter) and suitability for repeated measurements. Central to the RECIST 1.1 approach, a sum of the longest diameter (LD) for all target lesions is calculated. A more than 30% but less than 100% decrease in the sum of the LD of target lesions Figure 6. Longitudinal Tracking of Individual Metastasis in Circulating Tumor DNA (A) Arrows indicate the lines of therapy for patient (DONUM002). (B) Progression and response (as percentage of baseline, prior to T + L therapy) of individual lesions during treatment, colors correspond to individual lesions. BL: baseline; SD: stable disease; PD: progressive disease. (C) Schematic representation of individual lesion size and evolution during HERACLES (trastuzumab + lapatinib), T-DM1, and regorafenib treatments until patient s death. (D) The contribution of each metastasis normalized on the tumor load as detected in plasma ctdna (percentage of variations). The graph is based on NGS data from the indicated time points (indicated by the black triangles with white lines). The black bars indicate time points for which metastasis measurements by CT scan are available. Time points with no detectable tumor content were excluded. (E) Lesion-specific contribution to ctdna at the indicated time points (indicated by the black triangles with the white lines) during the overall course of treatments. Black lines indicate plasma time points matched with CT scan evaluations. (F) Graph showing dynamics of lesion-specific TCRs. The graph was constructed using TCR-seq data from sequencing of PBMCs collected at the indicated time points (black triangles with white lines). Black bars indicate time points for which CT scans were also available. N.D., not detected in liver metastasis. See also Figure S5, Tables S5 and S6. Cancer Cell 34, , July 9,

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13 corresponds to PR, while a more than 20% increase in the sum of target lesion LD corresponds to PD. RECIST rules were created more than 15 years ago as an international standard to quantify and categorize treatment effects (Therasse et al., 2000) and are widely applied: virtually all clinical trials evaluating the efficacy of new anticancer drugs are based on RECIST criteria. However, RECIST parameters stemmed from the outcome analysis of a warehouse of clinical trials of cytotoxic therapies and were not designed to properly interpret the impact of heterogeneous patterns of response to treatments others than chemotherapy. We reasoned that coupling canonical imaging with liquid biopsy-derived genomic information could improve outcome assessment in a precision medicine trial such as HERACLES. Indeed, in the last decade the influence of molecular heterogeneity on response to targeted therapies of solid cancer has become apparent based on detailed molecular profiles of individual metastatic deposits (de Bruin et al., 2014; Gerlinger et al., 2012, 2014; Gerlinger and Swanton, 2010). While pathologists had previously recognized that tumors are not morphologically homogeneous, only the availability of technologies capable of providing detailed maps of cancer molecular landscapes (such as NGS) have shed light on the role of molecular heterogeneity in cancers (Gerlinger et al., 2012; Park et al., 2010). We previously documented that treatment of mcrcs with targeted agents such as EGFR and BRAF inhibitors could result in lesion-specific responses (Oddo et al., 2017; Pietrantonio et al., 2016; Russo et al., 2016). These studies and those of others (Abbosh et al., 2017; Blakely et al., 2017; Murtaza et al., 2015) have highlighted that molecular heterogeneity triggers the emergence of unique drug-resistance patterns in distinct metastatic deposits. How can we accurately capture the evolutionary molecular landscapes that occur in distinct lesions of patients treated with anticancer agents? To tackle this issue, we focused on mcrc as a model system and exploited a unique set of blood and tissue samples collected during the HERACLES trial (Sartore-Bianchi et al., 2016). We then combined liquid biopsy with radiologic assessments to draw longitudinal maps of evolutionary trajectories for single metastases. Detection of plasma CNA is challenging given the typically low amounts of ctdna shed into the bloodstream and the overwhelming amount of leukocyte-derived DNA with a copy number of 2.0. The comprehensive ctdna platform utilized here correctly identified 50 of 52 samples as ERBB2 amplified. Building on these methodological premises, we asked whether we could exploit liquid biopsies to shed light on why some patients (despite having high HER2 expression) did not respond ab initio or relapsed after RECIST 1.1 confirmed response. NGS of ctdna coupled to RECIST measurements revealed two sets of resistance patterns: high-level VAFs in genes such as KRAS and BRAF, known to confer resistance to targeted agents in mcrcs, were sometimes already present in the baseline blood draw. We consider these as primary resistance cases, driven by highly represented resistant mutations that likely interfered with response to trastuzumab plus lapatinib (despite the presence of ERBB2 amplification). Interestingly, according to the HERACLES eligibility criteria, patients had to be RAS wild-type at diagnosis. However, patients entered HERACLES a median of 2.5 years after metastatic disease diagnosis, and all received anti-egfr treatment, which is known to drive the preferential emergence of RAS pathway mutations (Misale et al., 2012, 2014; Siravegna et al., 2015; Van Emburgh et al., 2016). Notably, had it been interventional (i.e., guiding the molecular eligibility of patients), liquid biopsy in HERACLES would have spared >85% of patients, who eventually resulted primary refractory, an ineffective treatment. The second pattern involved the emergence during therapy of resistance alleles. These had generally low VAF, indicating they were sub-clonal as compared with truncal alterations (APC and TP53) detected in the same blood draws. Our study presents the systematic identification of molecular alterations associated with clinical resistance to anti-her2 therapy in colorectal cancer. The findings suggest that the molecular landscape of resistance to HER2 blockade in colorectal and breast cancers are significantly different, as exemplified by the role of KRAS and BRAF in CRC. The positive results of the HERACLES study prompted the initiation of multiple clinical trials that are presently testing the efficacy of several anti-her2 regimens in CRC (including NCT , NCT , NCT , NCT , NCT ). It will be of great interest to compare the molecular landscapes of resistance to distinct HER2 blockade strategies in these studies with those we detected in patients who received the HERACLES regimen. Altogether this knowledge may prospectively help in selecting patients likely to respond to HER2 blockade. Radiologic assessment of individual lesions during HER2 blockade uncovered noteworthy evolutionary patterns, which on several occasions were organ and metastasis specific. In paradigmatic cases, progression was evident in one organ (lung) while response continued in another district (liver). These data point to a substantial heterogeneity of anatomical response Figure 7. Pharmaco-Genomic Analysis of Metastases-Specific Cell Models (A) Bar graph showing the ERBB2 and EGFR CNA in indicated DONUM002 PDX-derived cell models as assessed by ddpcr. (B) Correlation analysis of ERBB2 and EGFR CNA detected in different tissues and matched PDX-derived cell lines. (C) Representative immunofluorescence images of DONUM002 PDX-derived cell lines stained for HER2 (green) and EGFR (red). Nuclei were stained with DAPI (blue). Scale bar, 25 mm. (D) Immunoblot analysis of HER2, EGFR, and PTEN protein expression across the different DONUM002 PDX-derived cell lines. HSP90 was used as loading control. (E) Long-term growth assay of the indicated DONUM002 PDX-derived cell lines treated for 14 days with trastuzumab (TMAB, 25 mg/ml), lapatinib (LAP, 50 nm), or the combination of both drugs. Bar graph represents the quantification of crystal violet staining as percentage of the untreated control. Data are the average of at least two independent experiments, in replicate; error bars represent SD (p < 0.05, p < 0.01 by unpaired t test). (F) Long-term growth assay of the indicated DONUM002 PDX-derived cell lines treated for 14 days with trastuzumab-emtansine (T-DM1, 5 mg/ml), cetuximab (CMAB, 10 mg/ml), or combination of both drugs. Bar graph represents the quantification of crystal violet staining as percentage of the untreated control. Data are the average of at least two independent experiments, in replicate; error bars represent SD (p < 0.05, p < 0.01, p < by unpaired t test). See also Figure S4. Cancer Cell 34, , July 9,

14 and suggest that some lesions (sometimes entire organs) could have further benefited from the HERACLES therapy even after treatment was suspended based on RECIST 1.1 criteria. These results are particularly pertinent considering that, in several cases, metastases that displayed divergent responses to targeted HER2 blockade showed instead homogeneous response to the previous cycles of chemotherapy. Accordingly, while RECIST 1.1 criteria properly determined response to chemotherapy regimens, the same parameters failed to accurately capture patient- and lesion-specific benefits during targeted therapy. Our results suggest that, when targeted therapy is administered, all lesions (not just RECIST target lesions) should be monitored and considered in the decision whether to continue or suspend treatment. Although liquid biopsies and radiological assessments were pivotal in revealing mechanisms of resistance and lesion-specific responses within the HERACLES patient population, they are intrinsically incapable of shedding light on the biological and functional properties of anatomically distinct metastases. Furthermore, it is presently largely unknown whether and to what extent the clinical response of individual lesions translates into ctdna profiles. To address these issues, we studied a single patient who had responded and relapsed to the HERACLES therapy and had consented to a warm autopsy protocol. For this case, the molecular landscape of each of eight metastatic deposits was determined with high accuracy by WES, and the evolutionary trees of single lesions were retrieved. Personalized multiplex PCR assay panels targeting clonal and sub-clonal alterations allowed monitoring not only of the overall disease burden but also the contribution of individual lesions (and their sub-clonal components) in ctdna. There was a remarkable correlation between the size of each lesion and its contribution to the pool of ctdna in the blood, and patterns of expansion and reduction of the individual metastases were manifest in blood and could be correlated with clinical course. Interestingly, analysis of TCRseq in tumor tissues also revealed a high level of heterogeneity in TCR distribution and T cell infiltration intra- and inter-lesions. However, when metastases-specific TCR rearrangements were profiled in the blood, there was no direct correlation but rather an anti-correlation with ctdna patterns of the corresponding lesions. The interpretation of blood-based profiles derived from lesion-specific somatic SNVs (tracking epithelial cells) and TCRs (tracking infiltrating T lymphocytes) need further analyses. The availability of SNV and TCR profiles from multiple patients receiving distinct therapies will be central to defining the biological impact and the relationships among these parameters. Notably, distinct lesions displayed different TMBs and levels of predicted neoantigens. We speculate that higher TMB and NAL might be linked to the patterns of evolutionary trees and this in turn may be related to immune surveillance in some of the lesions. The molecular complexity of individual lesions was also evident by IHC and FISH analysis. Inter- and intra-lesion-specific heterogeneous patterns of HER2 and EGFR protein expression correlated with lesion-specific response and were recapitulated in cell models derived from individual lesions (and sub-lesions). While genomic maps, radiological measurements, and liquid biopsy profiles provide detailed snapshots of lesion-specific evolutionary patterns, they cannot directly inform on the functional and biological features of distinct metastases. Metastasis-specific cell models were therefore used to define biochemical landscapes and pharmacological vulnerabilities. Biochemical evidence indicates that, in most instances, HER2 expression was maintained at clinical progression. While we cannot rule out that antibody-dependent cell-mediated cytotoxicity plays a role in the clinical efficacy of trastuzumab, the clinical responses of individual lesions to the HERACLES therapy were retained in the matched cell models treated ex vivo with lapatinib and trastuzumab. Of potential clinical relevance, we found that the combinatorial blockade of both HER2 and EGFR via addition of the monoclonal antibodies cetuximab and T-DM1 was effective in suppressing the growth of most of the lesions. Although this work provides detailed insights into the evolution of metastatic CRC during HER2 blockade, our study has several limitations. For instance, the sample size of the anti-her2 treatment cohort, albeit sufficiently powered to draw statistically significant conclusions, was relatively small. In addition, we did not evaluate the role of ERBB3 (HER3) alterations in either primary or acquired resistance to lapatinib plus trastuzumab. The Cancer Genome Atlas reported a prevalence of 1.8% for ERBB2 mutation and 3.2% for ERBB3 mutation (The Cancer Genome Network, 2012), and mcrc has been shown to have a higher rate of ERBB3 mutations than most other cancers (Choi et al., 2014). If HER3 plays a role in primary or acquired resistance, then it may have implications for the choice of targeted combination therapies. For example, in breast cancers it has been shown that trastuzumab alone may not block ERBB3 ligand-induced ERBB2/ERBB3 heterodimerization, whereas this process is disrupted by addition of pertuzumab (Rexer and Arteaga, 2012). In addition, cell-free DNA (cfdna) NGS cannot properly assess copy number loss, which might have had a role in HERACLES resistance. Finally, we had access to autoptic samples from a single HERACLES patient; accordingly, further studies are needed to understand the general validity of our findings. Despite these limitations, our study sheds light on the mechanisms of resistance to trastuzumab and lapatinib, defines lesionspecific evolutionary trajectories during HER2 blockade, and puts forward a strategy to design additional lines of treatment in mcrc patients carrying ERBB2 amplification who relapse to the HERACLES therapy. Our results also suggest that coupling blood-derived genomic information to imaging might be highly valuable for guiding more informed treatment decisions. A comprehensive understanding of how genetically and anatomically different lesions respond and become resistant to therapy would leapfrog current guidelines, which are based exclusively on clinical parameters and are therefore biased by limited accessibility to global molecular and radiomics information. More importantly, understanding how individual lesions evolve under treatment and dynamically expose new vulnerabilities will enable adaptive therapeutic intervention, with positive consequences on the outlook of cancer patients. STAR+METHODS Detailed methods are provided in the online version of this paper and include the following: d KEY RESOURCES TABLE d CONTACT FOR REAGENT AND RESOURCE SHARING 160 Cancer Cell 34, , July 9, 2018