Elevated Hsp70 is associated with resistance to chemotherapy in several cancers (Brondani Da Rocha et al., 2004; Nanbu et al., 1998; Vargas-Roig et al., 1998), and Hsp70 overexpression correlates with metastasis in breast cancer murine models and in patients (Powers et al., 2010; Sun et al., 2008). To determine if Hsp70 activity is required for breast cancer cell survival, we performed a pilot study in which sensitivity to the Hsp70 inhibitor, MAL3-101 (Fewell et al., 2004), was measured in a panel of 14 breast cancer lines (Figure 1A and Table 1). MAL3-101 targets an allosteric site in the ATP-binding domain of Hsp70, specifically inhibits the ability of Hsp40 co-chaperones to activate Hsp70 ATPase activity, and has demonstrated in vitro and in vivo efficacy in Merkel Cell carcinoma and multiple myeloma as well as rhabdomyosarcoma cell lines (Adam et al., 2014; Braunstein et al., 2011; Sabnis et al., 2016; Sannino et al., 2018; Wisén et al., 2010). Among the luminal, HER2, and TNBC breast cancer subtypes, we were able to bin each line into sensitive, intermediate, and resistant groups (Figure 1A and Table 1). Breast cancer cells with an IC₅₀≤ 3.5 µM were considered MAL3-101 sensitive, whereas cells with an IC₅₀ ≥ 30 µM were considered resistant. IC₅₀s of breast cancer cells with an intermediate sensitivity were between 4 µM and 14 µM (Table 1). Interestingly, among identical breast cancer subtypes, breast cancer cells could be sensitive or insensitive to the Hsp70 inhibitor, indicating that other features dictate sensitivity to increased proteotoxic stress.

To confirm these results, we measured the levels of apoptosis markers in two representative MAL3-101-sensitive lines (Figure 1B,C; MDA MB 231 and MCF7, indicated in blue text) and resistant lines (MDA MB 453 and MDA MB 361, indicated in black text). In each group, one cell line represents a luminal cancer cell type (MCF7 and MDA MB 361) and one is categorized as a TNBC cell line (MDA MB 231 and MDA MB 453). Induction of apoptosis in the MAL3-101-sensitive line, MDA MB 231, was evident by the accumulation of cleaved caspase-3, caspase-7, and caspase-8. Because MCF7 cells are caspase-3 deficient (Kagawa et al., 2001; Turner et al., 2003), the cleavage of only caspase-7 was detected after Hsp70 inhibition (Figure 1B,C). In contrast, apoptotic markers were absent in the MDA MB 453 and MDA MB 361-resistant lines. MAL3-101 treatment also increased Hsp70 mRNA abundance (Figure 1—figure supplement 1A), and a variable but general increase in Hsp70 protein levels was apparent (Figure 1—figure supplement 1B) that failed to correlate with Hsp70 inhibitor sensitivity and resistance. The muted levels of Hsp70 protein induction compared to the massive increase (~20–40-fold) in gene expression are consistent with previous data in which a heat shock trigger was administered to cells in culture and the effects on Hsp70 protein and mRNA levels were examined (Petersen and Lindquist, 1989; Theodorakis and Morimoto, 1987). A general increase in Hsp70 mRNA is also consistent with MAL3-101-dependent inhibition of Hsp70, which compromises proteostasis (Sabnis et al., 2016; Sannino et al., 2018). In addition, when the steady-state abundance of cytoplasmic Hsp70 and the constitutively expressed isoform, Hsc70, were measured (Figure 1—figure supplement 1C), no correlation between protein levels and MAL3-101 sensitivity was apparent. These data strongly suggest that sensitivity/resistance arise from an independent phenomenon.

Hsp70 is a critical component of the Hsp90 ‘chaperome’ and facilitates the folding of oncogenic proteins such as BRAF, HER2, and AKT (Basso et al., 2002; Joshi et al., 2018; Sannino and Brodsky, 2017; Schopf et al., 2017; Whitesell and Lindquist, 2005; Xu et al., 2005). To examine whether Hsp90 and Hsp70 inhibitor sensitivities correlate, we applied the Hsp90 inhibitor 17-AAG (17-allylamino-17-demethoxy-geldanamycin, also known as tanespimycin) (Doddareddy et al., 2011; Schulte and Neckers, 1998; Taldone et al., 2009) to each cell line. In accordance with previous reports (Citri et al., 2004; Patel et al., 2013; Xu et al., 2005), HER2-expressing breast cancer cells were more sensitive to 17-AAG (Figure 1—figure supplement 1C and Table 1), but again there was no correlation with MAL3-101 sensitivity/resistance. Thus, the compounds appear to selectively affect different downstream effectors and compensatory mechanisms.

Hsp70 plays an essential role in cellular protein folding, degradation, transport, modification, and assembly (Balchin et al., 2016; Evans et al., 2010; Goloudina et al., 2012; Rosenzweig et al., 2019). Thus, reducing Hsp70 activity favors the accumulation of unfolded proteins. However, induction of compensatory proteostatic pathways, such as proteasome-dependent degradation and autophagy, might temper sensitivity to Hsp70 inhibitors. We previously demonstrated that rhabdomyosarcoma cells are hypersensitive to Hsp70 inhibition and acquired MAL3-101 resistance as a result of increased protein degradation pathway activity (Sannino et al., 2018). To define which compensatory pathway(s) might be altered and thus restrict breast cancer cell death in the presence of MAL3-101, we first asked if proteasome-dependant protein turnover was higher in MAL3-101 resistant lines. To this end, we measured the levels of ubiquitinated proteins in sensitive (MDA MB 231 and MCF7, in blue), intermediate (BT-474, in purple), and resistant (MDA MB 453 and MDA MB 361, in black) cells in the presence or absence of the proteasome inhibitor bortezomib (also known as Velcade) (Chen et al., 2011). As expected, bortezomib-mediated proteasome inhibition increased polyubiquitinated protein accumulation in all cell lines, but the level of total ubiquitinated proteins was greater in MAL3-101-resistant cells compared to cell lines with increased MAL3-101 sensitivity (Figure 2A, compare lanes 2 and 4 with lanes 8 and 10). These data suggest that resistant lines rely to a greater extent on the removal of misfolded proteins to survive.

Next, to determine whether proteasome activity might be higher in resistant cells, we prepared lysates and examined proteasome activity by measuring the MG 132-dependent signal produced from the fluorogenic substrate, Suc-LLVY-7-amino-4-methylcoumarin (Stein et al., 1996). As shown in Figure 2—figure supplement 1A, no statistically significant differences in proteasome activity were detected between the sensitive and resistant lines. These data indicate that even though MAL3-101-resistant cells rely more heavily on protein degradation pathways to mitigate the accumulation of misfolded proteins (Figure 2A), overall proteasome activity is unchanged in Hsp70 inhibitor sensitive and resistant cells under non-stress conditions. The increased accumulation of polyubiquitinated protein in MAL3-101-resistant cells might be due to a differential, compensatory involvement of the ubiquitination machinery between MAL3-101 sensitive and resistant lines upon proteasome inhibition, perhaps even reflecting altered E3 activity. Interestingly, however, the growth of the MAL3-101 sensitive and resistant lines displayed a range of sensitivities to the proteasome inhibitor bortezomib (Figure 2B and Table 1), but sensitivity still failed to correlate with Hsp70-inhibitor sensitivity. These results suggest that inhibition of the proteasome and Hsp70 kill breast cancer cells through independent mechanisms.

Another cellular quality control pathway that modulates proteostasis is the endoplasmic reticulum associated degradation pathway (ERAD). ERAD substrates are recognized by chaperones, including Hsp70, ubiquitinated at the ER membrane, retrotranslocated to the cytoplasm in a p97-dependent manner, and delivered to the proteasome (Bagola et al., 2011; Needham and Brodsky, 2013; Smith et al., 2011; Tsai and Weissman, 2011). It was previously reported that p97 expression is higher in HER2 positive breast cancer cells than other cells, suggesting that ERAD contributes to HER2 breast cancer cell survival (Singh et al., 2015). Therefore, we tested ERAD pathway dependence in resistant and sensitive breast cancer cells by applying a clinical drug candidate, CB-5083, which targets the p97 ATPase domain (Zhou et al., 2015). As shown in Figure 2—figure supplement 1B, minimal differences were detected in CB-5083 sensitivity between the lines (Table 1). Moreover, in contrast to previous work (Singh et al., 2015), the sensitivities of the examined HER2 positive and HER2 negative breast cancer cell lines were comparable (Table 1).

In contrast to ERAD, the autophagy pathway destroys not only misfolded and long-lived proteins, but also protein aggregates and damaged organelles (Amaravadi et al., 2016; B'chir et al., 2013; Cavalli and Cenci, 2020; Mizushima, 2017; Towers and Thorburn, 2016). During this process, cytoplasmic proteins are captured in double-membrane autophagosomes, and upon fusion with lysosomes the vesicle contents are degraded in autophagolysosomes (also termed autolysosomes). This pathway can be stimulated by cellular stressors, including nutrient or growth factor deprivation, the UPR, hypoxia, reactive oxygen species, DNA damage, drug treatments, or after pathogen invasion (He and Klionsky, 2009; Kroemer et al., 2010; Miracco et al., 2010; Rubinsztein et al., 2007; Wojcik, 2013). Moreover, autophagy can serve as a back-up for the ubiquitin-proteasome pathway (Fan et al., 2018; Kocaturk and Gozuacik, 2018; Wojcik, 2013) as well as the ERAD pathway (Ishida et al., 2009; Kruse et al., 2006). Therefore, we first asked if MAL3-101 sensitive and resistant breast cancer cells exhibited unique sensitivities to chloroquine (CQ) and bafilomycin, which block the final step in the autophagy pathway and impair the degradation of autophagosomal contents (Ahlberg et al., 1985; Klionsky et al., 2016; Manic et al., 2014; Mizushima et al., 2011). However, Hsp70 inhibitor resistance failed to correlate with increased CQ and bafilomycin sensitivity (Figure 2C and Figure 2—figure supplement 1C; also see Table 1). We next measured the abundance of key autophagy markers, LC3B and ATG5–ATG12 (Kabeya et al., 2000; Pyo et al., 2005). Immunoblot analysis showed greater amounts of ATG5–ATG12 in the resistant MDA MB 453 and MDA MB 361 lines compared to the MDA MB 231 and MCF7-sensitive cell lines (Figure 2D), suggesting that the elongation phase of autophagy was induced in resistant cells (Mizushima et al., 2011; Strømhaug et al., 2004). Consistent with this hypothesis, lipidated LC3B (LC3BII), which is appended to autophagosomal membranes, accumulated to a greater extent in the resistant (MDA MB 453 and MDA MB 361) cells when autophagy was inhibited by CQ or bafilomycin (Figure 2D, indicated by a red dot, and quantified in Figure 2—figure supplement 1D). Moreover, the conversion of LC3BI (the soluble LC3B isoform) into LC3BII was enhanced in resistant cells (Figure 2D and Figure 2—figure supplement 1E), as evidenced by the increased LC3BII:LC3BI ratio (Barth et al., 2010; McLeland et al., 2011; Zhang et al., 2016). The magnitude of this effect is consistent with other studies demonstrating autophagy induction (Klionsky et al., 2016; Li et al., 2018; Rouschop et al., 2010; Sannino et al., 2018).

Cancer cells induce autophagy to survive nutrient deprivation (Amaravadi et al., 2016; Klionsky, 2005; Mizushima et al., 2008). Thus, we asked whether autophagy pathway activation was also higher in resistant cells upon starvation. As shown in Figure 2—figure supplement 1F, LC3BII accumulated to a greater extent when CQ was added to resistant cells under short-term starvation in nutrient-depleted (EBSS) medium compared to sensitive lines. Together, these data indicate that autophagic flux is magnified in the inhibitor resistant (MDA MB 453 and MDA MB 361) breast cancer cells.

The data in the previous section suggest that misfolded proteins are directed to both the ubiquitin-proteasome and autophagy pathways, but only autophagy favors the survival of Hsp70 inhibitor resistant cells in the presence of MAL3-101. Therefore, we asked if modest inhibition of the autophagy and ubiquitin-proteasome pathways increased Hsp70 inhibitor sensitivity. First, MAL3-101 sensitivity in the presence of sub-maximal concentrations of either CQ or bortezomib in MAL3-101 sensitive and resistant lines was examined (see Table 2 for CQ and bortezomib doses). As shown in Figure 3A, little effect was seen in the sensitive (MDA MB 231 and MCF7) cells, but CQ more prominently increased MAL3-101 sensitivity in the resistant (MDA MB 453 and MDA MB 361) line. A similar phenomenon was observed when MAL3-101 was administered in presence of a sub-maximal dose of bafilomycin (Figure 3—figure supplement 1A and Tables 2 and 3). Second, we found that bortezomib modestly increased the sensitivity of both a sensitive (MDA MB 231) and a resistant (MDA MB 453) cell line, but to a significantly lesser extent compared to CQ (Figure 3B). No increase in sensitivity to MAL3-101 was apparent in the presence of bortezomib in the other lines (Figure 3B). Consequently, a combination of enhanced proteotoxic stress, via the addition of MAL3-101, and sub-optimal levels of autophagy can re-sensitize otherwise resistant cells to an Hsp70 inhibitor.

To support this conclusion, we silenced ATG5 in MAL3-101-resistant cells with two different siRNA oligonucleotides. ATG5 facilitates the autophagy elongation step and, together with ATG12 and ATG16L, links LC3BII to the growing autophagosomal membrane (Acevo-Rodríguez et al., 2020; Cuervo, 2004; Mizushima et al., 2011). While MAL3-101 sensitivities were unaffected by the transfection of a control siRNA, ATG5 knockdown decreased ATG5-12 abundance by ~70% and again re-sensitized the resistant cells to MAL3-101 (Figure 3—figure supplement 1B and C). As noted above, no significant changes in MAL3-101 sensitivity were detected in the MDA MB 231 sensitive cell line and only a slight increase in MCF7 cell sensitivity was apparent upon ATG5 knockdown.

Based on a potential compensatory role played by the autophagy pathway in cells with magnified proteotoxic stress, we investigated if autophagy is also acutely activated when Hsp70 function is muted. When the abundance of select autophagy-related genes and the gene encoding LC3B (MAP1LC3B) were measured 6 hr after MAL3-101 was added, the expression levels of ATG3, ATG5, ATG12, and MAP1LC3B were increased to a significantly higher extent in the resistant (MDA MB 453 and MDA MB 361) cell lines compared to the sensitive (MDA MB 231 and MCF7) cells (Figure 4A). Of note, the magnitude of the effect is in accordance with the increased levels of autophagy related genes detected in other studies (B'chir et al., 2013; Füllgrabe et al., 2016; Sannino et al., 2018). RNA collected from cells was also used to analyze the time-dependent, relative amplitude of the transcriptional response in the MAL3-101 sensitive and resistant cells 3 and 6 hr after Hsp70 was inhibited. Quantitative PCR analysis showed that each message rose over time to a greater degree in MAL3-101 resistant cells (Figure 4B). That autophagy was induced primarily in the resistant cells was confirmed by quantifying the abundance of LC3BII and the conversion rate of LC3BI in LC3BII when autophagy was inhibited with CQ (Figure 4C). In line with gene expression data, autophagy was also elevated more prominently in the resistant cells (MDA MB 453 and MDA MB 361) after Hsp70 activity was compromised with MAL3-101 for 6 hr (Figure 4C and D, see relative ‘CQ’ levels).

To better characterize the effect of Hsp70 inhibition on autophagic flux, we engineered a sensitive (MDA MB 231) and resistant (MDA MB 453) cell line to stably express an mRFP-GFP-tagged LC3B autophagy reporter (also known as tfLC3) (Kimura et al., 2007). Dual color analysis with this reporter provides a direct measure of autophagic flux (Figure 5A). In brief, when mRFP-GFP-LC3 decorated autophagosomes fuse with lysosomes, the lower intravesicular pH quenches GFP, permitting visualization of red fluorescence (via the mRFP fluorophore) and a rise in RFP⁺GFP^- puncta. An increase in the number of RFP⁺GFP^- puncta therefore indicates more robust delivery of autophagosomes to lysosomes, and consequently an increase in autophagolysosome formation (Klionsky et al., 2016; Zhang et al., 2016).

As shown in Figure 5B (and quantified in Figure 5C), Hsp70 inhibition favored the accumulation of RFP⁺GFP^- puncta, which are representative of increased autophagolysosome formation in resistant cells (MDA MB 453), but not in the MAL3-101 sensitive line (MDA MB 231). These data confirm that Hsp70 inhibition induces autophagy only in MAL3-101-resistant cells. In contrast, the percentage of RFP⁺GFP^- puncta in both MAL3-101 sensitive and resistant lines increased upon starvation in EBSS media (Figure 5B and C), indicating that autophagy flux remains competent in sensitive cells (Figure 5B and C). Because the percentage of RFP⁺GFP^- puncta per cell was comparable after MAL3-101 and EBSS treatment in the resistant (MDA MB 453) cells, the magnitude of the effect of MAL3-101 is akin to that triggered by nutrient depletion. We conclude that Hsp70 inhibition acutely induces autophagy in the MAL3-101-resistant line to compensate for proteotoxic stress.

We next investigated whether CQ and MAL3-101 not only prevented cell growth but also triggered apoptosis in resistant cells. MAL3-101 treatment alone was unable to induce apoptosis in the MAL3-101-resistant line (MDA MB 453). In contrast, cleaved caspase-3, caspase-7, and caspase-8 along with the pro-apoptotic protein, CHOP, accumulated when CQ was administrated for 2 hr to MAL3-101-treated cells (Figure 6A and Figure 6—figure supplement 1A). As anticipated by monitoring cell growth (Figure 3), induction of the apoptotic pathway in the MDA MB 231 (sensitive) cell line was unchanged regardless of whether MAL3-101 was added alone or in the presence of CQ (Figure 6A and Figure 6—figure supplement 1A).

To confirm these observations, annexin-V–propidium iodide (PI) staining (Koopman et al., 1994; Vermes et al., 1995) was used to analyze the percentage of apoptotic cells in the presence or absence of CQ and/or MAL3-101 treatment (Figure 6B). Although CQ-mediated inhibition of autophagy had no significant effect on the population of apoptotic cells in both MAL3-101 sensitive and resistant lines, addition of CQ for 2 hr increased the percentage of apoptotic cells when Hsp70 activity was impaired in the resistant cells (MDA MB 453 and MDA MB 361). In contrast, Hsp70 inhibition alone had no effect on the percentage of apoptotic-resistant cells. In addition—and also consistent with the data shown above—MAL3-101 initiated apoptosis in the sensitive (MCF7 and MDA MB 231) cells and the percentage of apoptotic cells were unaltered by CQ (Figure 6B). These data establish that combined application of Hsp70 and autophagy inhibitors kills resistant cells.

To better characterize the role of autophagy as a compensatory mechanism upon Hsp70 inhibition, we asked if autophagy induction was sufficient to reduce MAL3-101 sensitivity in the Hsp70 inhibitor-sensitive (MDA MB 231) cell line. The mTOR complex, together with AMPK and HIF1α, are the major autophagy regulators and serve as nutrient, energy, and growth factor sensors (Hindupur et al., 2015; Laplante and Sabatini, 2012). Thus, we first asked if MAL3-101 sensitive and resistant breast cancer cells exhibited unique sensitivities to the mTOR inhibitor, everolimus (also known as Afinitor), an FDA-approved rapamycin derivative that directly inhibits mTORC1 and indirectly inhibits mTORC2 (Chen and Zhou, 2020; Chiang and Abraham, 2007; Meng and Zheng, 2015). As shown in Figure 6C, negligible differences were seen in the sensitivities to everolimus between MAL3-101 sensitive and resistant cell lines (IC₅₀23.0 µM and 18.7 µM, respectively). Second, we monitored cell viability in Hsp70 inhibitor sensitive and resistant lines when MAL3-101 was administrated in the presence of sub-maximal doses (2 µM) of everolimus. We found that mTOR inhibition decreased MAL3-101 sensitivity in the MDA MD 231-sensitive line (Figure 6D). As expected, no change in MAL3-101 sensitivity was apparent in the presence of everolimus in resistant cells (MDA MB 453, Figure 6D). These data are consistent with the model that enhanced proteotoxic stress—via Hsp70 inhibition—and induction of the autophagy pathway protect MAL3-101 sensitive cells when challenged with an Hsp70 inhibitor.

Defects in chaperone activity cause profound changes in cancer cell proteostasis, leading to induction of the UPR and/or the ISR to modulate folding capacity and attenuate stress (Cubillos-Ruiz et al., 2017; Kim et al., 2015; Koromilas, 2015; Pakos-Zebrucka et al., 2016). A consequence of the accumulation of unfolded proteins in the ER is the increased expression of the UPR-related chaperone, BiP (Bertolotti et al., 2000; Walter and Ron, 2011). In fact, a modest but significant increase in the abundance of the message encoding BiP, HSPA5, was evident in each line examined 6 hr after MAL3-101 treatment, indicating that MAL3-101 mounts an ER stress response in both the MAL3-101 resistant and sensitive lines (Figure 7A, right panel), yet leads to cell death only in sensitive cells.

To dissect the contribution of each UPR branch on the effect of MAL3-101, we monitored the transcriptional response in MAL3-101 sensitive and resistant lines by quantifying the abundance of UPR related genes. To investigate the role of Ire1α, the degree to which spliced XbpI (sXbpI) accumulated after MAL3-101 administration was measured. A variable but significant accumulation of sXbpI, which is a product of the activated Ire1α endonuclease (Yoshida et al., 2001a), was detected after MAL3-101 treatment in both sensitive and resistant cells (Figure 7A). MCF7 cells exhibited the greatest accumulation of sXbpI upon MAL3-101 treatment, and, interestingly, ERN1 (the gene that encodes Ire1α) copy number is higher in this line (https://depmap.org/portal/). This is further reflected by the high level of increased Ire1α protein expression in these cell lines among the four lines examined (Figure 7—figure supplement 1A).

Next, we monitored the ATF6 branch of the UPR by quantifying the abundance of full-length ATF6 after addition of MAL3-101. Upon activation, full-length ATF6 translocates to the Golgi apparatus where it is cleaved to produce an active, soluble ATF6 transcription factor (Haze et al., 1999; Shen et al., 2002). Thus, a decrease in full-length ATF6 isoform is consistent with enhanced ATF6 cleavage and induction. As shown in Figure 7B, the abundance of the ATF6 full-length isoform declined in the presence of DTT, which activates each branch of the UPR, but was unchanged between the MAL3-101 sensitive (MCF7 and MDA MB 231) and resistant (MDA MB 453 and MDA MB 361) breast cancer cells after MAL3-101 treatment.

We then monitored the presence of the DNA damage inducible transcript 3 (DDIT3), which encodes CHOP, as well as the mRNA corresponding to ATF4, which are induced downstream of the p-eIF2α/PERK pathway (Koromilas, 2015; Walter and Ron, 2011). While ATF4 mRNA was induced to variable levels after MAL3-101 treatment in both the MAL3-101 sensitive and resistant lines, DDIT3 accumulated only in the sensitive (MDA MB 231 and MCF7) cell line (Figure 7C and Figure 7—figure supplement 1B). This result is consistent with the fact that a pro-apoptotic response is elicited selectively in lines with increased sensitivity to MAL3-101. Because CHOP acts downstream of the p-eIF2α response (Marciniak et al., 2004; Palam et al., 2011), we then quantified the message encoding another pro-apoptotic factor, PUMA (also known as BBC3), which is transcriptionally activated by ATF4–CHOP (Galehdar et al., 2010; Matus et al., 2013). Consistent with the data shown in Figure 7C, BBC3 (PUMA) mRNA also rose after MAL3-101 treatment in the sensitive but not resistant lines, thereby confirming an apoptotic response in the MAL3-101 sensitive MCF7 and MDA MB 231 lines (Figure 7—figure supplement 1C).

To further establish the contribution of the p-eIF2α/PERK pathway in mediating the apoptotic response in select breast cancer cell lines, we measured MAL3-101-dependent changes in select proteins in sensitive and resistant cells. As shown in Figure 7D and quantified in Figure 7—figure supplement 1D, p-eIF2α accumulated to a similar extent in MAL3-101 sensitive and resistant cells when Hsp70 was inhibited and was indistinguishable when compared to the level observed when cells were treated with DTT. In contrast, and consistent with quantitative PCR data (Figure 7C and Figure 7—figure supplement 1B), significant CHOP induction was detected only in Hsp70 inhibitor sensitive cells (MCF7 and MDA MB 231) after MAL3-101 addition (Figure 7E and Figure 7—figure supplement 1E).

Together, the activation of each branch of the UPR—Ire1α, ATF6, and PERK—is consistent with the fact that the sensitive and resistant cells can induce the UPR, but a protective autophagy-dependent response resides selectively in resistant cells. Based on these data, we suggest that resistant cells have been rewired to better counteract proteotoxic insults.

To define the role of Ire1, if any, on MAL3-101-dependent apoptosis, Ire1α was silenced and sensitive (MDA MB 231) and resistant (MDA MB 453) cell survival was examined after MAL3-101 was added. We first noted that less than ~25–35% of Ire1α protein remained 48 hr after siRNA transfection (Figure 8A and B), a level similar to that observed in other systems when this message was silenced (Li et al., 2017; Luhr et al., 2019; Mu et al., 2008; Notte et al., 2015). In addition—and as anticipated—sXbpI RNA abundance fell when Ire1α was silenced (Figure 8C). In contrast, Hsp70 protein abundance was unaffected when Ire1α was silenced (Figure 8A), indicating the lack of compensatory Hsp70-dependent effects on cellular homeostasis (also see Figure 1—figure supplement 1). In addition, the relative levels of p-eIF2α were unaffected by the loss of Ire1α after MAL3-101 was added to either sensitive or resistant cells, as expected since phosphorylation is induced downstream of PERK. Finally, MAL3-101 only increased the levels of CHOP in sensitive cells (MDA MB 231), consistent with data shown in Figure 7—figure supplement 1E; nevertheless, the response was again unaffected by the absence of Ire1α (Figure 8A and D, compare CTRL and Ire1αi samples).

The data outlined above implicate the UPR/ISR in the MAL3-101-dependent response that can lead to cell death. In mammals, four kinases (PERK, GCN2, PKR, and HRI) are linked to the ISR and eIF2α phosphorylation and—as a result to their diverse roles as sensors— myriad stress stimuli can be produced via the ISR (Ryoo and Vasudevan, 2017; Wek, 2018). PKR is induced upon viral infection and participates in the lysosomal clearance of misfolded proteins (Darini et al., 2019; Pataer et al., 2020; Wek, 2018), GCN2 activation depends on the ribosome P-stalk after amino acid depletion (Harding et al., 2019; Inglis et al., 2019; Ye et al., 2010), and the HRI kinase triggers the ISR when mitochondrial function is altered or heme is depleted. It was also reported that HRI contributes to the general cytosolic unfolded protein response (Chen and London, 1995; Guo et al., 2020; Mukherjee et al., 2021; Zhang et al., 2019). Thus, we focused on whether HRI, PERK, PKR, and/or GCN2 were required for MAL3-101 resistance. To this end, we transfected two different siRNAs directed against each kinase. Knockdown efficiency was assessed by immunoblot analysis 72 hr after transfection.

We first investigated the role of HRI during the induction of MAL3-101-mediated apoptosis. siRNA oligo transfection decreased HRI expression levels by 80% in both the MDA MB 231 and MDA MB 453 cells (Figure 9A and B), a level comparable to that observed in other systems when HRI was silenced (White et al., 2018; Zhang et al., 2019). However, HRI knockdown failed to increase Hsp70 abundance, indicating the absence of a compensatory response to MAL3-101 in sensitive and resistant cells. We next measured the apoptotic response upon MAL3-101 treatment in control and HRI knockdown cells. As shown in Figure 9A and quantified in Figure 9C, HRI silencing modestly increased the accumulation of cleaved caspase-8 and CHOP, but not cleaved caspase-3 (compare CTRL and HRIi samples in Figure 9A) in the MAL3-101 sensitive line, MDA MB 231. However, this effect was not statistically significant. Moreover, when HRI expression was blunted in the resistant (MDA MB 453) cells, MAL3-101 failed to induce an apoptotic response. Thus, we conclude that HRI is dispensable for the survival of MAL3-101 resistant cells, but might contribute minimally to MAL3-101-dependent apoptosis in sensitive cells.

Next, we investigated the role of other two ISR sensors, PERK and PKR. Knockdown using targeted siRNAs against PERK and PKR decreased levels by 85% in MDA MB 231 cells and by 85% (PKR) and 70% (PERK) in MDA MB 453 cells (Figure 10A and B). Comparable knockdown efficiencies were observed in other systems (Luhr et al., 2019; Mu et al., 2008; Pataer et al., 2020; Tang et al., 2019). Nevertheless, to show that these knockdown conditions were sufficient, we found that p-eIF2α and CHOP levels fell when PERK was silenced compared to control transfected samples in sensitive and resistant cells treated with the UPR stressor tunicamycin (Figure 10—figure supplement 1B).

Having validated the knockdown conditions, we next measured the induction of apoptotic markers under control or knockdown conditions and in the presence or absence of MAL3-101. As shown in Figure 10A and quantified in Figure 10—figure supplement 1A (see relative ‘CTRL’ levels), PKR silencing had no effect on the accumulation of cleaved caspase-3, cleaved caspase-8, or CHOP, but in contrast reduced PERK activity decreased the levels of the apoptotic markers in sensitive (MDA MB 231) cells in the presence of MAL3-101. However, when PERK or PKR was silenced in the resistant (MDA MB 453) cells, an apoptotic response was absent when MAL3-101 was added. To confirm PERK activation upon MAL3-101 treatment, its phosphorylation status (p-PERK) was monitored by examining whether mobility was slowed after SDS-PAGE and immunoblotting (Figure 10C). We found that PERK mobility was reduced to a similar extent in MAL3-101 sensitive and resistant cells when Hsp70 was inhibited with MAL3-101 or when cells were treated with tunicamycin. These data confirm that PERK is activated in both MAL3-101 sensitive and resistant cells upon Hsp70 inhibition, yet PERK is only responsible for drug-dependent apoptosis in the sensitive cell lines.

To confirm the contribution of PERK in MAL3-101-mediated apoptosis in sensitive cells, we pre-treated sensitive and resistant cells with a PERK inhibitor, GSK-2606414 (‘GSK’), prior to MAL3-101 administration (Axten et al., 2012). We then monitored the induction of apoptosis in the presence or absence of PERK inhibition when Hsp70 activity was impaired (MAL3-101 addition) or in control (DMSO) conditions (Figure 10D and Figure 10—figure supplement 1D). In line with our data, above, on the role played by PERK in initiating apoptosis in the sensitive cell line, GSK reduced MAL3-101-mediated apoptosis in MDA MB 231 cells upon Hsp70 inhibition (Figure 10D and Figure 10—figure supplement 1D). In contrast, and also in line with the data presented above, PERK activity was dispensable for triggering MAL3-101-driven cell death in the resistant MDA MB 453 cell line.

Finally, we examined the role of GCN2 in breast cancer cell survival upon Hsp70 inhibition. Only ~25% of GCN2 remained after knockdown in either cell line (Figure 11A and B), a level comparable to that observed in other systems in which the contribution of this kinase on cellular stress responses was analyzed (Rajanala et al., 2019; Shi et al., 2019). We subsequently measured the accumulation of CHOP, cleaved caspase-3, and cleaved caspase-8 in MAL3-101-sensitive cells (MDA MB 231) after GCN2 was silenced, but the levels of these reporters were unaltered (Figure 11A and C). Notably, GCN2 knockdown failed to increase Hsp70 levels, indicating that a compensatory response to MAL3-101 was absent in the sensitive cells.

We next asked if GCN2 plays a role in the pro-survival response in MDA MB 453 resistant cells. In contrast to the modest effect of PERK and PKR on eIF2α phosphorylation, p-eIF2α levels significantly decreased when GCN2 was silenced in these cells in the presence of MAL3-101 (Figure 11A and C, see relative ‘CTRL’ levels). GCN2 knockdown also induced apoptosis when Hsp70 activity was impaired with MAL3-101 (Figure 11A and C). Interestingly, the phosphorylation of Thr-899 in the activation-loop of GCN2 was unaltered by MAL3-101 addition (pGCN2, Figure 11—figure supplement 1A). This result suggests that other sites might be phosphorylated when Hsp70 activity is impaired. By analogy, a previous report hinted that other residues might be phosphorylated in response to ribosome P-stalk-mediated activation of GCN2, and multiple GCN2 phosphorylation events were detected upon amino acid starvation which were independent of Thr-899 phosphorylation (Harding et al., 2019). In addition, the increased accumulation of CHOP upon MAL3-101 treatment when GCN2 expression was silenced suggests that apoptosis might depend on the activation of another ISR sensor (Figure 11A and C) in resistant (MDA MB 453) cells. Regardless, these results position the GCN2-dependent phosphorylation of eIF2α as a trigger for protection against compromised proteostasis and the induction of a pro-survival degradation pathway, that is, autophagy, in MAL3-101-sensitive cells.

To confirm the role of the ISR in MAL3-101-associated apoptosis, we monitored apoptotic induction in the presence or absence of the ISR inhibitor, ISRIB (Halliday et al., 2015; Rabouw et al., 2019; Sidrauski et al., 2015; Zyryanova et al., 2018). ISRIB is an allosteric inhibitor of the downstream eIF2α target, eIF2B, and ultimately blocks the ISR (Zyryanova et al., 2018). We found that ISRIB had no effect on p-eIF2α accumulation (Figure 11—figure supplements 1A and B, compare ‘MAL3-101’ and ‘MAL3-101+ISRIB’), yet ISRIB treatment decreased CHOP, cleaved caspase-3, and cleaved caspase-8 accumulation in sensitive (MDA MB 231) cells after MAL3-101 was added (Figure 11—figure supplements 1A and C, see relative ‘MAL3-101’ levels). When the ISR was inhibited with ISRIB in the resistant MDA MB 453 cells, only a modest accumulation of cleaved caspase-8 was induced when MAL3-101 was present (Figure 11—figure supplements 1A and C). Thus, taken together with the data presented above, these results identify the ISR as an apoptotic trigger in sensitive breast cancer cell lines when proteostasis is compromised.

Hsp70 inhibition induces the accumulation of p-eIF2α to a similar extent in MAL3-101 sensitive and resistant breast cancer lines, but the eIF2α kinases (PERK versus GCN2) and the final cellular outcomes (apoptosis versus autophagy, respectively) are distinct: PERK knockdown blunted the death response in sensitive cells after MAL3-101 treatment, whereas the silencing of GCN2 favored the death of resistant cells after the same treatment. To better define the roles of the PERK and GCN2 kinases in the induction of the compensatory (i.e., autophagy) response in resistant cells, we monitored autophagy activation in control and PERK and GCN2 silenced cells in the presence or absence of MAL3-101. In this experiment, CQ was added during the last 2 hr of MAL3-101 treatment to block autophagolysosome-dependent degradation, which allowed us to focus on autophagic flux under each condition.

First, we confirmed that autophagy—as measured by both the level of LC3BII and the LC3BII:LC3BI ratio—was present at a higher level in resistant (MDA MB 453) compared to sensitive cells (MDA MB 231) transfected with a control siRNA and were either treated or left untreated with MAL3-101 (Figure 12A, and data quantified in Figure 12—figure supplement 1A,B; see relative ‘CTRL’ levels). Next, we found that the accumulation of the autophagy marker LC3BII and the LC3BII:LC3BI ratio decreased in CQ-treated resistant cells (MDA MB 453) in which PERK expression was blunted compared to control cells. This result suggests that PERK enhances basal autophagic flux in MAL3-101-resistant cells. However, MAL3-101 treatment still increased the amount of LC3BII as well as the LC3BII:LC3BI ratio, suggesting that PERK is dispensable for MAL3-101-mediated autophagy induction in the resistant cells. In contrast, and as anticipated, there was no significant change in LC3BII accumulation and the LC3BII:LC3BI ratio between control and PERK siRNA transfected sensitive (MDA MB 231) cells in the presence of MAL3-101 (Figure 12A, Figure 12—figure supplement 1A,B; compare ‘MAL3-101’ CTRL with ‘MAL3-101’ PERKi).

Second, we asked if GCN2 activates autophagy in the presence or absence of MAL3-101. In resistant cells (MDA MB 453), the accumulation of the autophagy marker LC3BII and the LC3BII:LC3BI ratio decreased when GCN2 was silenced and when Hsp70 was inhibited, suggesting that GCN2 is critical for MAL3-101-mediated autophagy induction (Figure 12B, Figure 12—figure supplement 1B; note decreased signal for ‘MAL3-101’, ‘GCN2i’). On the contrary, no change in autophagy marker abundance was detected in resistant cells (MDA MB 453) treated only with CQ, suggesting that the basal autophagy flux is unaltered by GNC2 silencing (Figure 11B, Figure 12—figure supplement 1B). Autophagy was also unaltered in sensitive cells (MDA MB 231) upon GCN2 protein ablation (Figure 12B, Figure 12—figure supplement 1A; ‘GCN2i’). These data confirm the role of GCN2 in autophagy induction when proteotoxic stress rises in resistant cells.

PERK partially compensates for autophagy induction in GCN2^-/- MEF cells under stress conditions (Ye et al., 2010). Therefore, to better define the role of PERK and GCN2 in MAL3-101-mediated autophagy in sensitive and resistant cells, we performed a PERK-GCN2 double knockdown and compared both LC3BII accumulation and the LC3BI:LC3BII ratio to the control (Figure 12C). Genetic ablation of the two eIF2α kinases impaired basal and MAL3-101-mediated autophagy in MAL3-101 resistant (MDA MB 453), but not in sensitive (MDA MB 231) cells (Figure 12C, Figure 12—figure supplement 1A and B; ‘PERKi GCN2i’). Moreover, the knockdown of both PERK and GCN2 showed an additive effect as LC3BII accumulation along with LC3BI to LC3BII conversion in resistant cells (MDA MB 453) decreased even more compared to the single knockdowns, at least in the absence of MAL3-101 (Figure 12C and Figure 12—figure supplement 1B). Taken together, our data demonstrate that PERK oversees autophagy in the absence of Hsp70 inhibition, but GNC2 is fundamental for MAL3-101-dependent autophagy in resistant cells.

To better define the difference in the sensitivities of breast cancer cells to MAL3-101, we analyzed publicly available RNA sequencing (RNAseq) databases from the Broad Institute Cancer Cell Line Encyclopedia (CCLE) (Barretina et al., 2012) and RNAseq data published in prior work (Marcotte et al., 2016). We also evaluated the breast cancer cell proteome using the public available RPPA datasets (Marcotte et al., 2016; Figure 13A and B). Interestingly, Hsp70 inhibitor sensitivity failed to correlate with the transcriptional profiles of the 14 breast cancer cells examined in our study (Figure 13A). On the contrary, the RPPA dataset analysis revealed that resistant breast cell lines (MDA MB 453, MDA MB 361 and HCC38) have higher levels of activated AMPK (AMPKα-pT172) and of the ribosomal S6 kinase (p70-S6K-pT389) (Figure 13B). These proteins, respectively, are direct autophagy inducers in response to energy availability and regulate translation elongation downstream of mTOR signaling (Hindupur et al., 2015). Increased pT172 AMPK levels also suggest that MAL3-101-resistant cells exploit AMPK activation to modulate their metabolic state to maintain an adequate energy balance even under proteotoxic stress conditions (Garcia and Shaw, 2017). In agreement with this hypothesis, both increased pT172 AMPK and p70-S6K-pT389 levels accumulate upon glucose starvation in HEK293 cells when autophagy is induced (Kim et al., 2011). Thus, MAL3-101-resistant cells are primed to modulate metabolism (i.e. inducing autophagy) to counteract imbalanced proteostasis.

To further define the pathways involved in the differential MAL3-101 response between sensitive and resistant cells, we also performed an RNAseq analysis after sensitive (MDA MB 231) and resistant (MDA MB 453) cells were treated with MAL3-101 or the DMSO control. MAL3-101 treatment had a minor impact on the global transcriptional profile of the resistant cells (MDA MB 453, samples A), whereas significantly more variability in the transcriptional profile of MDA MB 231 cells was detected when comparing the MAL3-101 and DMSO-treated samples (samples B, Figure 13C). In agreement with the global transcriptomic analysis, above, hierarchical clustering of the samples based on differential gene analysis revealed that MAL3-101-treated sensitive cells (MDA MB 231) cluster with resistant MDA MB 453 cells (Figure 13D), while no change was visible in between MAL3-101 and DMSO treated resistant cells (MDA MB 453). These transcriptomic analyses suggest that MAL3-101 sensitivity is more tightly linked to changes in the cancer cell proteome and metabolome compared to the transcriptional profiles. This hypothesis will be pursued in future work. In fact, the top 35 genes differentially regulated between sensitive and resistant cells failed to correlate with any specific molecular mechanism or biological process (Figure 13D).

Finally, to identify other factors that might dictate ISR-related circuits in MAL3-101 sensitive cells, we compared the two sample cohorts in MDA MB 231 cells to assess the presence of genes that may drive MAL3-101 sensitivity. We found that protein folding and the ER stress response pathways were upregulated in MAL3-101-sensitive cells, MDA MB 231, when Hsp70 activity was inhibited (Figure 13E). These data confirm the key role of the UPR and, in particular, of the PERK branch of this pathway in dictating the fate of MAL3-101 sensitive cells when Hsp70 activity is inhibited.

MDA MB 453, MDA MB 468, HCC1419, MCF7, MDA MB 231, HCC1395, BT-474, MDA MB 361, SKBR-3, T47D, HCC38, MDA MB 134, and HCC1937 were purchased from ATCC, and SUM44PE was purchased from Asterand Bioscience Inc (Detroit, MI). Cells were tested to be mycoplasm-free by use of a standard diagnostic PCR using the ATCC universal mycoplasma detection kit. Cell authentication was performed using the University of Arizona Genetics Core according to their protocol. All cell lines were grown at 37°C and 5% CO₂. The HCC38, HCC1419, MDA MB 231, MDA MB 453, HCC1395, HCC1937, and MDA MB 468 lines were grown in RPMI-1640 media (#SH30027.FS, GE Healthcare Hyclone, Logan, UT) supplemented with 10% FBS (#SH30071.03, GE Healthcare Hyclone) and 1x penicillin/streptomycin (#15-140-122, Gibco, Thermo Fisher Scientific, Waltham, MA), while BT-474, MDA MB 361, MCF7, T47D cells were grown in DMEM-Hi glucose media with sodium pyruvate (#D6429, Millipore Sigma, Saint Louis, MO) which was supplemented with 10% FBS (GE Healthcare Hyclone) and 1x penicillin/streptomycin (Gibco, Thermo Fisher Scientific, Waltham, MA). SKBR-3 cells were cultured in McCoy's 5A with L-Glutamine media (#SH30200FS, Thermo Fisher Scientific, Waltham, MA) supplemented with 10% FBS (GE Healthcare Hyclone) and 1x penicillin/streptomycin (Gibco, Thermo Fisher Scientific, Waltham, MA). MDA MB 134 cells were grown in L-15 (Leibovitz) media (#L5520, Millipore Sigma, Saint Louis, MO) with 10% FBS (GE Healthcare Hyclone) and 1x penicillin/streptomycin (Gibco, Thermo Fisher Scientific, Waltham, MA), and SUM44PE cells were cultured in DMEM/F-12 media (#11330057, Gibco, Thermo Fisher Scientific, Waltham, MA) supplemented with 2% Charcoal/Dextran stripped FBS (#100–119, Gemini Bio-Products, West Sacramento, CA), along with 5 μg/mL human recombinant insulin (#I2643), 1 μg/mL hydrocortisone solution (#H6909), 5 mM ethanolamine (#E0135), 5 μg/mL human apo-transferrin (#T2252), 6.6 ng/mL 3',3',5-triiodo-l-thyronine sodium salt (#T5518), and 8.7 ng/mL sodium selenite (#S9133), all purchased from Millipore Sigma, Saint Louis, MO.

MAL3-101 (Fewell et al., 2004), MG 132 (EMD Millipore, Burlington, MA), everolimus (Sigma-Aldrich, Saint Louis, MO), 17-AAG, and CB-5082 (Selleck Chemicals, Houston, TX) were dissolved in DMSO to a final concentration of 20 mM, while bafilomycin (Selleck Chemicals, Houston, TX) and bortezomib (Cell Signaling Technology, Danvers, MA) were prepared at a final concentration of 1 mM in DMSO. ISRIB and tunicamycin (Sigma-Aldrich, Saint Louis, MO) were suspended to a final concentration of 10 mg/mL and GSK-2606414 (GSK; Tocris Bioscience, Bristol, UK) was dissolved to a final concentration of 50 mM in DMSO. Chloroquine (CQ; Sigma-Aldrich, Saint Louis, MO) was dissolved in sterile water to a final concentration of 100 mM. All compounds were stored at −20°C. Prior to use, the desired amount of each compound was added to pre-warmed media, mixed thoroughly, and added directly onto cells.

To silence ATG5, cells were seeded at a density of 150,000 in a six-well plate, and after 24 hr, two different siRNA oligos against ATG5 (siGENOME9474, D-004374–03 and D-004374–05, Dharmacon, Lafayette, CO) or a control siRNA (BLOCK-iT Alexa Fluor Red Fluorescent Control, Invitrogen, Thermo Fisher Scientific) were transfected using Lipofectamine RNAiMAX (Invitrogen, Thermo Fisher Scientific) at 20 nM according to the manufacturer’s instructions. After 6 hr, cells were harvested and seeded at a density of 1,500 cells/100 µL in 96-well clear-bottomed plates (Greiner bio-one, North Carolina) for cell viability assays (see below). The remaining transfected cells were collected for protein extraction 72 hr post-transfection to detect knock-down efficiency, as described below. The optimal time frame and concentration of siRNA were selected after conducting time course experiments in which ATG5-12 and Hsp70 protein abundance were analyzed by immunoblot (data not shown).

To silence specific ISR sensors, MDA MB231 and MDA MB 453 cells were seeded at a density of 1,000,000 in a 10 cm dish, and after 24 hr two oligos for PERK (EIF2AK3), PKR (EIF2AK2) (#s18101 and #s18103, #s11186 and #s11185, Ambion Fisher Scientific Waltham, MA) or GCN2 (EIF2AK4) (#D-005314–16, #D-005314–13, Dharmacon, Lafayette, CO), or one oligo for HRI (EIF2AK1) (#D-005007–04) or a control siRNA (BLOCK-iT Alexa Fluor Red Fluorescent Control, Invitrogen, Thermo Fisher Scientific) were transfected using Lipofectamine RNAiMAX (Invitrogen, Thermo Fisher Scientific) at 20 nM according to the manufacturer’s instructions. After 48 hr, cells were harvested and seeded at a density of 150,000 cells/well in a six-well plate. After 24 hr, cells were treated with the indicated compounds and processed for protein extraction, as described below. In the experiment where both GCN2 and PERK were knocked down 5 nM of each siRNA oligo (#s18101 and #s18103, #D-005314–16 and D-005314–13) where transfected using Lipofectamine RNAiMAX and the samples where processed as indicated before in the single knock down experiments.

For experiments in which the UPR transducer Ire1α was knocked down, MDA MB 231 and MDA MB 453 cells were seeded at a density of 1,200,000 in a 10 cm dish, and after 24 hr, a set of oligos at 10 nM for Ire1α (ERN1) (#s200430, Ambion Fisher Scientific, Waltham, MA) or a control siRNA were transfected as above. After 24 hr, cells were harvested and seeded at a density of 150,000 cells/well in a six-well plate, and after 24 hr, cells were treated with the indicated compounds and processed for protein or RNA extraction (see below).

To transfect the plasmid encoding the RFP-GFP-LC3B tandem autophagy reporter (provided by Dr. Charlene Chu, Department of Pathology, University of Pittsburgh) (Kimura et al., 2007), MDA MB 231 and MDA MB 453 cells were seeded at a density of 1,200,000 in 10 cm dish, and after 24 hr, a total of 12 µg of the plasmid was introduced using the FuGENE6 reagent (Promega, Madison, WI) according to the manufacturer’s instructions. Cells were selected for G418 resistance (350 µg/mL, GE Healthcare Hyclone) for 8 days, and fresh G418-containing media was added every 3 days. Once a G418 resistant polyclonal population was obtained, the cells were trypsinized and resuspended at a density of 1 cell/100 μL in a 96-well plate. After 2 weeks, single clones where isolated, expanded, and tested for RFP-GFP-LC3B reporter expression by microscopy. MAL3-101 sensitivity in the obtained clones was tested as described in assays for cell viability (see below; two clones for MDA MB 231 and MDA MB 453 lines, data not shown). One clone for each line was used to test the autophagy induction rate upon Hsp70 inhibition in MAL3-101. Each RFP-GFP-LC3B expressing clone was seeded at a density of 200,000 cells in poly-d-Lysine Coated 35 mm MatTek dishes (P35GC-1.5–10 C, MatTek Corporation, Ashland, MA). After 24 hr, cells were independently treated with EBSS, MAL3-101 (5 µM), or vehicle (DMSO) for 22 hr. The samples were observed with a Nikon A1 point scanning confocal microscope without fixation to measure autophagosome and autophagolysosome formation by monitoring the distribution and alteration of RFP and GFP fluorescent signal after the indicated treatments. Complete volumes of cells were acquired at 0.2 μm steps, and volumes were reconstructed and analyzed using Nikon’s NIS-Elements software (Nikon Instrument, Melville, NY). Bright spot detection tool was used to identify and quantify the number of RFP and GFP positive (‘dots’) per cell after equalizing signal intensity suing the LUTs - Non-destructive Image Enhancement tool. A non-parametric Kruskal-Wallis test analysis was performed using Prism software (GraphPad, La Jolla, CA). Asterisks in figures indicate statistically significant differences between the samples. To obtain representative images from this experiment, maximum intensity projections of 0.2 μm steps though the entire cell were generated using Nikon’s NIS-Elements software. The scale shown is 10 µm.

Cells were plated at the indicated densities in Table 2 in 96-well clear-bottomed plates (Greiner bio-one, North Carolina), and after 72 hr, of the indicated treatment the cells were lysed and incubated with the CellTiter-Glo reagent (Promega) and luminescence was read on a Bio-Rad ChemiDoc XRS+ with the associated Image Lab software (Bio-Rad, Thermo Fisher Scientific). Under conditions in which cells were treated with more than one compound (e.g. Figure 3A and B; Figure 3—figure supplement 1A; Table 3), the cells were incubated with a range of doses of MAL3-101 in the presence of subcritical doses of bortezomib, everolimus, CQ, or bafilomycin that induced no greater than 30% of cell death in each line 72 hr after treatment at these concentrations (see Table 2). Viability was quantified after normalizing values obtained after bortezomib, CQ, or bafilomycin treatment alone at the indicated concentrations (Tables 2 and 3). When viability was measured after ATG5 knock-down, MAL3-101 sensitivity was measured 48 hr after treatment and 72 hr after siRNA transfection.

Analysis of cell surface Annexin-V was performed by staining cells with the Annexin-V Apoptosis Detection Kit (eBioscience, Thermo Fisher Scientific) following the manufacturer’s instructions. In brief, cells were seeded at a density of 220,000 cells/well for the MDA MB 361 line and at 150,000 cells/well for the other indicated lines in 6-well plates and allowed to adhere overnight before performing the indicated treatments. After mild trypsinization, the cells were harvested and washed in ice-cold PBS and then equilibrated in Annexin-V binding buffer. A cell resuspension of 150,000 cells in 100 μL was incubated with 5 µL of Annexin-V APC for 15 min in the dark. After three washes with binding buffer, the cells were suspended in 500 μL of Annexin-V binding buffer containing 5 μL of Propidium iodide (PI) and analyzed on an Accuri C6 FACS apparatus (BD-Biosciences, San Diego). Single-cell staining was used to discriminate Annexin-V- and PI-positive cells, respectively. Annexin-V and Annexin-V/PI double positive cells were summed and are represented as the percentage of apoptotic cells.

All antibodies used in this study were obtained from Cell Signaling Technology (Danvers, MA), unless indicated otherwise. To measure the levels of endogenous proteins, cells were plated at a density of 150,000 cells/well for all cell lines except for MDA MB 361 cells (220,000 cells/well) in six-well plates and allowed to adhere overnight before the indicated treatments. The supernatant/media was collected, and after the cells were washed in PBS cell lysates were obtained after resuspending adherent cells in 1% SDS RIPA buffer (100 mM Tris-HCl, pH 7.5, 1% NP40, 1% SDS, 300 mM NaCl and 0.5% sodium deoxycholate) supplemented with protease inhibitors (cOmplete Mini EDTA free tablets; Roche, Indianapolis, IN), phosphatase inhibitors (PhosSTOP, phosphatase inhibitor tablets; Millipore Sigma, Saint Louis, MO), 10 mM N-ethylmaleimide (NEM; Sigma-Aldrich), and 5 mM phenylmethanesulfonyl fluoride (PMSF; Sigma-Aldrich) on ice. The lysate was clarified by sonication and centrifugation at 13,000 g for 5 min at 4°C, and total protein was quantified with the BCA assay kit (Thermo Fisher Scientific). Aliquots containing 35 µg of total protein were incubated in sample buffer at 95°C for 5 min, subjected to SDS-PAGE using a 15% polyacrylamide gel, and after transfer to PVDF membranes (Immobilon-FL, #IPFL85E, Sigma-Aldrich) the blots were incubated with the following antibodies: anti-LC3B (2775S; at 1:1000), anti-ATG5 (D5F5U, #12994S; at 1:1000), anti-p62/SQSTM1 (P0067, Sigma-Aldrich; at 1:2000), anti-CHOP (L63FZ, #2895S; at 1:500), anti-cleaved Caspase-3 (#9661S; at 1:750), and anti-cleaved Caspase-7 (D6H1, #8438S; at 1:500). For the remaining proteins aliquots from the same lysates were instead heated to 37°C for 30 min prior to SDS-PAGE using 4–20% Tris-Glycine gradient gels (XP04202Box, Thermo Fisher Scientific), and after transfer on nitrocellulose membranes (#84–874, Prometheus Laboratories Inc, California), the blots were incubated with anti-cleaved Caspase-8 (18C8, #9496; at 1:1000), anti-BiP (C50B12, #3177S; at 1:1000), anti-Hsp70 (smc-113, StressMarq Biosciences, Victoria, British Columbia; at 1:1000), anti-Hsc/Hsp70 (#4872S; at 1:1000), anti-HER2 (29D8, #2165; at 1:2000), anti-eIF2α (#9722S; at 1:2000), anti-phospho-eIF2α (#9721L; at 1:500), anti-PERK (D11A8, #5683S; at 1:2000), anti-PKR (D7F7, #12297S; at 1:2000), anti-Ire1α (14C10, #3294S; at 1:1000), anti-HRI (MBS2538114, MyBioSource, San Diego, CA; at 1:500) and anti-GCN2 and anti-GCN2 pT899 antibody (ab-134053 and ab-75836, Abcam, Cambtidge, UK; at 1:2000). Anti-β-actin (ab-6276, Abcam, Cambtidge, UK; at 1:5000) was used as loading control. All primary antibodies were incubated overnight at 4°C. Anti-rabbit immunoglobulin G (IgG) and anti-Mouse immunoglobulin HRP-conjugated secondary antibodies (at 1:4000) were applied for 2 hr at room temperature prior to imaging. In all cases, proteins were visualized using a mix of ProSignal Pico and ProSignal Femto ECL Reagent at a 1:1 ratio (Prometheus Laboratories Inc, California) and images were taken using a Bio-Rad ChemiDoc XRS+ with Image Lab software. Data were analyzed using ImageJ software (National Institutes of Health, NIH).

To measure LC3BII accumulation and LC3BI to LC3BII conversion in the presence or absence of MAL3-101, cells were seeded as above in six-well plates, and after 24 hr, the cells were treated with 12 µM MAL3-101 or with DMSO for 6 hr. A total of 2 hr before the end of the 6 hr time point, 250 µM CQ was added for the remaining 2 hr. A corresponding volume of vehicle (i.e. water) was added for the CQ mock treated samples. Cells were processed and LC3B abundance was detected as described above. The CQ concentration was selected after conducting experiments with increasing doses (50, 100, 150, 200, 250, 300, and 350 µM) in which LC3BII accumulation was analyzed by immunoblot and apoptosis induction was monitored by cleaved caspase-3 and caspase-7 appearance (data not shown). The CQ concentration, which gave the highest LC3BII accumulation but no significant apoptosis induction after a 2 hr treatment, was selected.

To induce non-selective (macro) autophagy, cells were washed twice in sterile PBS to remove any remaining media and then starved for 6 hr in Earle's Balanced Salt Solution (EBSS, Thermo Fisher Scientific). A total of 2 hr before the end of the 6 hr time point, 250 µM CQ was added for 2 hr. A corresponding volume of vehicle (i.e. water) was added for the mock treated samples.

To inhibit PERK, cells were pre-treated with GSK-2606414 at a final concentration of 2 µM for 2 hr in complete media and then 12 µM MAL3-101 or a corresponding volume of vehicle (DMSO) was added for 6 hr. To inhibit the ISR pathway, resistant and sensitive cells were treated with 10 µM ISRIB for for 10 hr and 15 µM MAL3-101 or a corresponding volume of DMSO was added for 6 hr before the 10 hr endpoint. Cells were processed as described above.

To detect levels of total protein ubiquitination, cells were seeded at the density mentioned above in six-well plates and allowed to adhere overnight, before they were treating with 18 nM bortezomib for 4 hr. Samples were collected and processed as above and 10 μg of total protein was subject to SDS-PAGE and proteins were transferred to nitrocellulose. Prior to incubating the blots in a milk solution, the nitrocellulose membranes were incubated in a boiling water bath for 1 hr to expose antibody epitopes. An anti-ubiquitin antibody, P4D1 (Santa Cruz Biotechnology, Dallas, TX), was used at 1:1000, and anti-mouse immunoglobulin G (IgG) HRP-conjugated secondary antibodies (at 1:4000) were applied for 2 hr at room temperature prior to imaging.

To detect ATF6, cells were plated at a density of 220,000 cells/well for the MDA MB 361 cells and 150,000 cells/well for the other cell lines in six-well plates and allowed to adhere overnight. Next, the cells were treated as indicated and 2 mM DTT was applied for 1 hr as a positive control. All treatments were performed in the presence of 5 μM MG 132 to avoid ATF6 degradation (Horimoto et al., 2013). The supernatant/medium was then collected, and after the cells were washed in PBS containing 5 μM MG 132, the cells were detached with trypsin, centrifuged in a clinical centrifuge, and the cell pellets were resuspended in 10 mM Tris-HCl, pH 7.5, 150 mM NaCl, 10 μM MG 132, and 1% SDS buffer, supplemented with protease inhibitors (cOmplete Mini EDTA free tablets; Roche, Indianapolis, IN), 10 mM NEM (Sigma-Aldrich), and 5 mM PMSF (Sigma-Aldrich). The lysate was then clarified by sonication and centrifugation at 13,000 g for 5 min at 4°C, and total protein was quantified with the BCA assay kit as described above. A total of 50 μg protein was incubated at 95°C for 5 min, subjected to SDS-PAGE using a 8% polyacrylamide gel (acrylamide:Bis-acrylamide, 29:1, 40% solution, OmniPur, Sigma-Millipore, Germany), and the resulting nitrocellulose filter was incubated in Tris-buffered saline with 0.1% Tween 20 (TBST) containing 10% non-fat dry milk for 1 hr at room temperature with gentle shaking. The nitrocellulose filter was then incubated with an aliquot of anti-ATF6 antibody (73–500, Bio Academia, Japan; at 1:1000) in the presence of 5% non-fat dried milk powder for 1 hr at room temperature. An anti-light chain-specific anti-mouse IgG monoclonal antibody (115-035-174, Jackson ImmunoResearch, West Grove, PA) was then used as the HRP-conjugated secondary antibody at 1:5000 in 1% non-fat milk TBST for 2 hr at room temperature. Decorated proteins were visualized using the ProSignal Femto ECL Reagent kit (Prometheus Laboratories Inc, California) and images were taken using a Bio-Rad ChemiDoc XRS+ with Image Lab software. Full-length ATF6 was quantified as an indicator of ATF6-mediated UPR induction using ImageJ software.

Cells were seeded at 150,000 cells/well for MDA MB 231, MDA MB 453, and MCF7 cells and at 220,000 cells/well for the MDA MB 361 line in six-well plates, grown overnight, and then treated for the indicated times with the indicated compounds. RNA was extracted using the RNeasy kit (Qiagen, Hilden, Germany) according to the manufacturer’s instructions. cDNA was synthetized from 1 µg of the extracted RNA using qScript cDNA SuperMix reverse transcriptase kit (Quantabio, Beverly, MA), and 70 ng were used for Real Time qPCR using the QuantStudio 3 (Thermo Fisher Scientific). Each PCR was performed on three or more biological replicates and with three technical replicates for each reaction. Primer efficiency was determined by serial dilution of the template and the relative expression ratios were calculated (Pfaffl, 2001). Primers amplifying β-actin were used as an internal control. All primer sequences were published in previous work (Sabnis et al., 2016; Sannino et al., 2018).

Cell lysates from MDA MB 231, MDA MB 453, MDA MB 361, and MCF7 cells were collected as described (Milan et al., 2015), and proteasome activity was assessed by monitoring the production of 7-amino-4-methylcoumarin (AMC) from the Suc-LLVY-AMC proteasome substrate (cat. no. I-1395; Bachem, Torrance, CA), which specifically detects the chymotrypsin-like activity of the proteasome (Stein et al., 1996). To this end, 10 µL aliquots of each lysate were incubated with 1 µL of a 5 mM stock solution of Suc-LLVY-AMC in 20 mM Tris-HCl, pH 7.5, 2 mM ATP, 2 mM MgCl₂, and 0.2% bovine serum albumin in the presence or absence of 10 µM MG 132 (Gleixner et al., 2017; Sannino et al., 2018). The fluorescence of released AMC was measured at time 0 and after 30 min in a Cytation5 plate reader at an excitation wavelength of 380 nm and an emission wavelength of 460 nm (BioTek, Winooski, Vermont). To calibrate the assay, a standard free fluorophore solution containing a range of AMC concentrations was used (VWR, Randor, PA). All measurements were performed in duplicate and values were normalized to protein content as determined by the BCA protein assay kit according to the manufacturer’s instructions (Thermo Fisher Scientific). Proteasome activity was calculated by: ((RFU2 – RFU1) / (T2-T1)) / [protein]. One unit of proteasome activity is defined as the amount of proteasome that generates 1.0 picomol of AMC per min at 37°C.

MDA MB 231 and MDA MB 453 cells were seeded at 150,000 cells/well in six-well plates, grown overnight, and then treated for 6 hr with a final concentration of 12 µM MAL3-101 or an equal volume of DMSO. RNA was extracted using the RNeasy kit (Qiagen, Hilden, Germany) according to the manufacturer’s instructions. Each total RNA extraction was performed on three biological replicates. RNA sequencing library construction, quality control, and PE150 sequencing were performed by Novogene Co., Ltd. according to their protocol. Transcript counts from all samples were quantified with Salmon v. 1.3.0 (Patro et al., 2017), converted to gene-level counts with tximport (Soneson et al., 2015), and were then used in the DESeq2 function: ‘DESeqDataSetFromTximport’ (Love et al., 2014).

Differential gene expression analysis was performed using the DESeq2 package (Love et al., 2014). Principal component analysis plots were generated from variance stabilizing transformed (VST) DESeq2 data-frames. For RNAseq datasets corresponding to MAL3-101 treated and untreated cells, the top 35 VST differentially expressing genes were displayed in Figure 13D. Differentially expressed genes with a p-value below 0.05 were used for Gene Ontology analysis applying the ‘clusterProfiler’ package (Yu et al., 2012; Figure 13E). The RNAseq data is deposited onto the Gene Expression Omnibus database (GEO, GSE178352). All raw data are available upon request from the corresponding authors.

For publicly available RNAseq datasets, RNAseq fastq files from the 14 breast cancer cell lines tested for MAL3-101 sensitivity in Figure 1A were downloaded with GEO Accession Numbers GSE36133 (CCLE) or GSE73526 (Barretina et al., 2012; Marcotte et al., 2016). Transcript counts from all samples were quantified with Salmon v.0.12.0 (Patro et al., 2017) and converted to gene-level counts with tximport (Soneson et al., 2015). Differential gene expression analysis was performed using the DESeq2 package (Love et al., 2014). Publicly available RPPA data was obtained from the Neel Lab (http://neellab.github.io/simem/) (Marcotte et al., 2016) and normalized Log₂ median centered reads were used for analysis. Data visualization was performed using ‘ggpubr’ and the CRAN ‘heatmap’ packages.

IC₅₀ concentrations from CellTiter-Glo assays were calculated as described (Sabnis et al., 2016) using SigmaPlot 11.0 (Systat Software, Inc). GraphPad Prism (GraphPad Software, Inc) was used to carry out a two-tailed Student’s t test between two samples and a non-parametric Kruskal-Wallis test analysis was performed to establish statistical significance between control and compound-treated samples in the same line for the RFP-GFP-LC3B autophagy reporter assay (Figure 5). In all experiments, p<0.05 was considered significant with the exception of the data presented in Figure 13B, where protein expression levels between resistant and sensitive cell lines with a p-value below 0.1 were displayed.